{ "cells": [ { "cell_type": "code", "execution_count": 1, "id": "95b97231-bcb2-4fe5-967c-e3b4b86031aa", "metadata": {}, "outputs": [ { "data": { "text/html": [ "'F:/dev/git/PLOSC/testdata'" ], "text/latex": [ "'F:/dev/git/PLOSC/testdata'" ], "text/markdown": [ "'F:/dev/git/PLOSC/testdata'" ], "text/plain": [ "[1] \"F:/dev/git/PLOSC/testdata\"" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "getwd()" ] }, { "cell_type": "code", "execution_count": 2, "id": "8a63494c-51af-4873-b329-0f34028925da", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Downloading GitHub repo mjoppich/PLOSC@HEAD\n", "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\u001b[36m──\u001b[39m \u001b[36mR CMD build\u001b[39m \u001b[36m─────────────────────────────────────────────────────────────────────────────────────────────────────────\u001b[39m\n", "\u001b[32m✔\u001b[39m \u001b[90mchecking for file 'C:\\Users\\mjopp\\AppData\\Local\\Temp\\RtmpU1Va3H\\remotes19f1019ea4b24\\mjoppich-PLOSC-badcb2e/DESCRIPTION'\u001b[39m\u001b[36m\u001b[39m\n", "\u001b[90m─\u001b[39m\u001b[90m \u001b[39m\u001b[90mpreparing 'PLOSC':\u001b[39m\u001b[36m\u001b[39m\n", "\u001b[32m✔\u001b[39m \u001b[90mchecking DESCRIPTION meta-information\u001b[39m\u001b[36m\u001b[39m\n", "\u001b[90m─\u001b[39m\u001b[90m \u001b[39m\u001b[90mchecking for LF line-endings in source and make files and shell scripts\u001b[39m\u001b[36m\u001b[39m\n", "\u001b[90m─\u001b[39m\u001b[90m \u001b[39m\u001b[90mchecking for empty or unneeded directories\u001b[39m\u001b[36m\u001b[39m\n", "\u001b[90m─\u001b[39m\u001b[90m \u001b[39m\u001b[90mbuilding 'PLOSC_0.0.0.9000.tar.gz'\u001b[39m\u001b[36m\u001b[39m\n", " \n", "\r" ] } ], "source": [ "remotes::install_github(\"mjoppich/PLOSC\", force=TRUE, upgrade=\"never\")" ] }, { "cell_type": "code", "execution_count": 3, "id": "7011a6f7", "metadata": { "lines_to_next_cell": 2 }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "The legacy packages maptools, rgdal, and rgeos, underpinning the sp package,\n", "which was just loaded, will retire in October 2023.\n", "Please refer to R-spatial evolution reports for details, especially\n", "https://r-spatial.org/r/2023/05/15/evolution4.html.\n", "It may be desirable to make the sf package available;\n", "package maintainers should consider adding sf to Suggests:.\n", "The sp package is now running under evolution status 2\n", " (status 2 uses the sf package in place of rgdal)\n", "\n", "Attaching SeuratObject\n", "\n" ] } ], "source": [ "\n", "# install.packages(\"hdf5r\")\n", "# BiocManager::install(c(\"biomaRt\", \"clusterProfiler\", \"ReactomePA\", \"org.Hs.eg.db\", \"org.Mm.eg.db\", \"ComplexHeatmap\", \"enrichplot\", \"EnhancedVolcano\"))\n", "# remotes::install_github(\"mjoppich/PLOSC\")\n", "\n", "library(\"Seurat\")\n", "library(\"PLOSC\")\n" ] }, { "cell_type": "code", "execution_count": 4, "id": "3a8f42c9", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[1] \"20094_0001 T:/scdata/covid_sc_cellranger/h5files/20094_0001_A_B_raw_feature_bc_matrix.new.h5\"\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Genome matrix has multiple modalities, returning a list of matrices for this genome\n", "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[1] \"WITH AB 20094_0001\"\n", "[1] \"20094_0001 36601 x 6794880 genes x cells\"\n", "[1] \"20094_0002 T:/scdata/covid_sc_cellranger/h5files/20094_0002_A_B_raw_feature_bc_matrix.new.h5\"\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Genome matrix has multiple modalities, returning a list of matrices for this genome\n", "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[1] \"WITH AB 20094_0002\"\n", "[1] \"20094_0002 36601 x 6794880 genes x cells\"\n", "[1] \"20094_0003 T:/scdata/covid_sc_cellranger/h5files/20094_0003_A_B_raw_feature_bc_matrix.new.h5\"\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Genome matrix has multiple modalities, returning a list of matrices for this genome\n", "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[1] \"WITH AB 20094_0003\"\n", "[1] \"20094_0003 36601 x 6794880 genes x cells\"\n", "[1] \"20094_0004 T:/scdata/covid_sc_cellranger/h5files/20094_0004_A_B_raw_feature_bc_matrix.new.h5\"\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Genome matrix has multiple modalities, returning a list of matrices for this genome\n", "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[1] \"WITH AB 20094_0004\"\n", "[1] \"20094_0004 36601 x 6794880 genes x cells\"\n", "[1] \"20094_0005 T:/scdata/covid_sc_cellranger/h5files/20094_0005_A_B_raw_feature_bc_matrix.new.h5\"\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Genome matrix has multiple modalities, returning a list of matrices for this genome\n", "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[1] \"WITH AB 20094_0005\"\n", "[1] \"20094_0005 36601 x 6794880 genes x cells\"\n", "[1] \"20094_0006 T:/scdata/covid_sc_cellranger/h5files/20094_0006_A_B_raw_feature_bc_matrix.new.h5\"\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Genome matrix has multiple modalities, returning a list of matrices for this genome\n", "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[1] \"WITH AB 20094_0006\"\n", "[1] \"20094_0006 36601 x 6794880 genes x cells\"\n", "[1] \"20094_0007 T:/scdata/covid_sc_cellranger/h5files/20094_0007_A_B_raw_feature_bc_matrix.new.h5\"\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Genome matrix has multiple modalities, returning a list of matrices for this genome\n", "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[1] \"WITH AB 20094_0007\"\n", "[1] \"20094_0007 36601 x 6794880 genes x cells\"\n", "[1] \"20094_0008 T:/scdata/covid_sc_cellranger/h5files/20094_0008_A_B_raw_feature_bc_matrix.new.h5\"\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Genome matrix has multiple modalities, returning a list of matrices for this genome\n", "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[1] \"WITH AB 20094_0008\"\n", "[1] \"20094_0008 36601 x 6794880 genes x cells\"\n", "[1] \"20094_0009 T:/scdata/covid_sc_cellranger/h5files/20094_0009_A_B_raw_feature_bc_matrix.new.h5\"\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Genome matrix has multiple modalities, returning a list of matrices for this genome\n", "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[1] \"WITH AB 20094_0009\"\n", "[1] \"20094_0009 36601 x 6794880 genes x cells\"\n", "[1] \"20094_0012 T:/scdata/covid_sc_cellranger/h5files/20094_0012_A_B_raw_feature_bc_matrix.new.h5\"\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Genome matrix has multiple modalities, returning a list of matrices for this genome\n", "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[1] \"WITH AB 20094_0012\"\n", "[1] \"20094_0012 36601 x 6794880 genes x cells\"\n" ] } ], "source": [ "\n", "files <- Sys.glob(\"T:/scdata/covid_sc_cellranger/h5files/*.h5\")\n", "inputMatrices = readH5Files(files, sample_element=5, sample_processor=function(x){return(substr(x, 1, 10))})\n" ] }, { "cell_type": "code", "execution_count": 5, "id": "b94b086b", "metadata": { "lines_to_next_cell": 2 }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[1] \"Renaming Cells\"\n", "[1] \"Seurat obj project 20094_0001\"\n", "[1] \"Got a total of mt-Genes: 13 MT-ND1, MT-ND2, MT-CO1, MT-CO2, MT-ATP8, MT-ATP6\"\n", "[1] \"Got a total of Rpl-Genes: 54 RPL22, RPL11, RPL5, RPL31, RPL37A, RPL32\"\n", "[1] \"Got a total of Rps-Genes: 49 RPS6KA1, RPS8, RPS27, RPS6KC1, RPS7, RPS27A\"\n", "[1] \"Got a total of Rp-Genes: 103 RPL22, RPL11, RPS6KA1, RPS8, RPL5, RPS27\"\n", "[1] \"20094_0001\"\n", "An object of class Seurat \n", "36601 features across 6794880 samples within 1 assay \n", "Active assay: RNA (36601 features, 3000 variable features)\n", "[1] \"Renaming Cells\"\n", "[1] \"Seurat obj project 20094_0002\"\n", "[1] \"Got a total of mt-Genes: 13 MT-ND1, MT-ND2, MT-CO1, MT-CO2, MT-ATP8, MT-ATP6\"\n", "[1] \"Got a total of Rpl-Genes: 54 RPL22, RPL11, RPL5, RPL31, RPL37A, RPL32\"\n", "[1] \"Got a total of Rps-Genes: 49 RPS6KA1, RPS8, RPS27, RPS6KC1, RPS7, RPS27A\"\n", "[1] \"Got a total of Rp-Genes: 103 RPL22, RPL11, RPS6KA1, RPS8, RPL5, RPS27\"\n", "[1] \"20094_0002\"\n", "An object of class Seurat \n", "36601 features across 6794880 samples within 1 assay \n", "Active assay: RNA (36601 features, 3000 variable features)\n", "[1] \"Renaming Cells\"\n", "[1] \"Seurat obj project 20094_0003\"\n", "[1] \"Got a total of mt-Genes: 13 MT-ND1, MT-ND2, MT-CO1, MT-CO2, MT-ATP8, MT-ATP6\"\n", "[1] \"Got a total of Rpl-Genes: 54 RPL22, RPL11, RPL5, RPL31, RPL37A, RPL32\"\n", "[1] \"Got a total of Rps-Genes: 49 RPS6KA1, RPS8, RPS27, RPS6KC1, RPS7, RPS27A\"\n", "[1] \"Got a total of Rp-Genes: 103 RPL22, RPL11, RPS6KA1, RPS8, RPL5, RPS27\"\n", "[1] \"20094_0003\"\n", "An object of class Seurat \n", "36601 features across 6794880 samples within 1 assay \n", "Active assay: RNA (36601 features, 3000 variable features)\n", "[1] \"Renaming Cells\"\n", "[1] \"Seurat obj project 20094_0004\"\n", "[1] \"Got a total of mt-Genes: 13 MT-ND1, MT-ND2, MT-CO1, MT-CO2, MT-ATP8, MT-ATP6\"\n", "[1] \"Got a total of Rpl-Genes: 54 RPL22, RPL11, RPL5, RPL31, RPL37A, RPL32\"\n", "[1] \"Got a total of Rps-Genes: 49 RPS6KA1, RPS8, RPS27, RPS6KC1, RPS7, RPS27A\"\n", "[1] \"Got a total of Rp-Genes: 103 RPL22, RPL11, RPS6KA1, RPS8, RPL5, RPS27\"\n", "[1] \"20094_0004\"\n", "An object of class Seurat \n", "36601 features across 6794880 samples within 1 assay \n", "Active assay: RNA (36601 features, 3000 variable features)\n", "[1] \"Renaming Cells\"\n", "[1] \"Seurat obj project 20094_0005\"\n", "[1] \"Got a total of mt-Genes: 13 MT-ND1, MT-ND2, MT-CO1, MT-CO2, MT-ATP8, MT-ATP6\"\n", "[1] \"Got a total of Rpl-Genes: 54 RPL22, RPL11, RPL5, RPL31, RPL37A, RPL32\"\n", "[1] \"Got a total of Rps-Genes: 49 RPS6KA1, RPS8, RPS27, RPS6KC1, RPS7, RPS27A\"\n", "[1] \"Got a total of Rp-Genes: 103 RPL22, RPL11, RPS6KA1, RPS8, RPL5, RPS27\"\n", "[1] \"20094_0005\"\n", "An object of class Seurat \n", "36601 features across 6794880 samples within 1 assay \n", "Active assay: RNA (36601 features, 3000 variable features)\n", "[1] \"Renaming Cells\"\n", "[1] \"Seurat obj project 20094_0006\"\n", "[1] \"Got a total of mt-Genes: 13 MT-ND1, MT-ND2, MT-CO1, MT-CO2, MT-ATP8, MT-ATP6\"\n", "[1] \"Got a total of Rpl-Genes: 54 RPL22, RPL11, RPL5, RPL31, RPL37A, RPL32\"\n", "[1] \"Got a total of Rps-Genes: 49 RPS6KA1, RPS8, RPS27, RPS6KC1, RPS7, RPS27A\"\n", "[1] \"Got a total of Rp-Genes: 103 RPL22, RPL11, RPS6KA1, RPS8, RPL5, RPS27\"\n", "[1] \"20094_0006\"\n", "An object of class Seurat \n", "36601 features across 6794880 samples within 1 assay \n", "Active assay: RNA (36601 features, 3000 variable features)\n", "[1] \"Renaming Cells\"\n", "[1] \"Seurat obj project 20094_0007\"\n", "[1] \"Got a total of mt-Genes: 13 MT-ND1, MT-ND2, MT-CO1, MT-CO2, MT-ATP8, MT-ATP6\"\n", "[1] \"Got a total of Rpl-Genes: 54 RPL22, RPL11, RPL5, RPL31, RPL37A, RPL32\"\n", "[1] \"Got a total of Rps-Genes: 49 RPS6KA1, RPS8, RPS27, RPS6KC1, RPS7, RPS27A\"\n", "[1] \"Got a total of Rp-Genes: 103 RPL22, RPL11, RPS6KA1, RPS8, RPL5, RPS27\"\n", "[1] \"20094_0007\"\n", "An object of class Seurat \n", "36601 features across 6794880 samples within 1 assay \n", "Active assay: RNA (36601 features, 3000 variable features)\n", "[1] \"Renaming Cells\"\n", "[1] \"Seurat obj project 20094_0008\"\n", "[1] \"Got a total of mt-Genes: 13 MT-ND1, MT-ND2, MT-CO1, MT-CO2, MT-ATP8, MT-ATP6\"\n", "[1] \"Got a total of Rpl-Genes: 54 RPL22, RPL11, RPL5, RPL31, RPL37A, RPL32\"\n", "[1] \"Got a total of Rps-Genes: 49 RPS6KA1, RPS8, RPS27, RPS6KC1, RPS7, RPS27A\"\n", "[1] \"Got a total of Rp-Genes: 103 RPL22, RPL11, RPS6KA1, RPS8, RPL5, RPS27\"\n", "[1] \"20094_0008\"\n", "An object of class Seurat \n", "36601 features across 6794880 samples within 1 assay \n", "Active assay: RNA (36601 features, 3000 variable features)\n", "[1] \"Renaming Cells\"\n", "[1] \"Seurat obj project 20094_0009\"\n", "[1] \"Got a total of mt-Genes: 13 MT-ND1, MT-ND2, MT-CO1, MT-CO2, MT-ATP8, MT-ATP6\"\n", "[1] \"Got a total of Rpl-Genes: 54 RPL22, RPL11, RPL5, RPL31, RPL37A, RPL32\"\n", "[1] \"Got a total of Rps-Genes: 49 RPS6KA1, RPS8, RPS27, RPS6KC1, RPS7, RPS27A\"\n", "[1] \"Got a total of Rp-Genes: 103 RPL22, RPL11, RPS6KA1, RPS8, RPL5, RPS27\"\n", "[1] \"20094_0009\"\n", "An object of class Seurat \n", "36601 features across 6794880 samples within 1 assay \n", "Active assay: RNA (36601 features, 3000 variable features)\n", "[1] \"Renaming Cells\"\n", "[1] \"Seurat obj project 20094_0012\"\n", "[1] \"Got a total of mt-Genes: 13 MT-ND1, MT-ND2, MT-CO1, MT-CO2, MT-ATP8, MT-ATP6\"\n", "[1] \"Got a total of Rpl-Genes: 54 RPL22, RPL11, RPL5, RPL31, RPL37A, RPL32\"\n", "[1] \"Got a total of Rps-Genes: 49 RPS6KA1, RPS8, RPS27, RPS6KC1, RPS7, RPS27A\"\n", "[1] \"Got a total of Rp-Genes: 103 RPL22, RPL11, RPS6KA1, RPS8, RPL5, RPS27\"\n", "[1] \"20094_0012\"\n", "An object of class Seurat \n", "36601 features across 6794880 samples within 1 assay \n", "Active assay: RNA (36601 features, 3000 variable features)\n" ] } ], "source": [ "\n", "objlist.raw = toObjList(inputMatrices, patternList_human(), 3000)\n", "\n" ] }, { "cell_type": "code", "execution_count": 6, "id": "87ea6cec", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[1] \"20094_0001\"\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Rasterizing points since number of points exceeds 100,000.\n", "To disable this behavior set `raster=FALSE`\n", "\n", "Rasterizing points since number of points exceeds 100,000.\n", "To disable this behavior set `raster=FALSE`\n", "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[1] \"20094_0001_scatter_ncount_mt 10 6\"\n", "[1] \"Saving to file 20094_0001_scatter_ncount_mt.png\"\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Warning message:\n", "\"\u001b[1m\u001b[22mRemoved 5852225 rows containing missing values (`geom_scattermore()`).\"\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[1] \"Saving to file 20094_0001_scatter_ncount_mt.pdf\"\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Warning message:\n", "\"\u001b[1m\u001b[22mRemoved 5852225 rows containing missing values (`geom_scattermore()`).\"\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[1] \"Saving to file 20094_0001_scatter_ncount_mt.svg\"\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Warning message:\n", "\"\u001b[1m\u001b[22mRemoved 5852225 rows containing missing values (`geom_scattermore()`).\"\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[1] \"Saving to file 20094_0001_scatter_ncount_mt.data\"\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Rasterizing points since number of points exceeds 100,000.\n", "To disable this behavior set `raster=FALSE`\n", "\n", "Rasterizing points since number of points exceeds 100,000.\n", "To disable this behavior set `raster=FALSE`\n", "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[1] \"20094_0001_scatter_ncount_rp 10 6\"\n", "[1] \"Saving to file 20094_0001_scatter_ncount_rp.png\"\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Warning message:\n", "\"\u001b[1m\u001b[22mRemoved 5852225 rows containing missing values (`geom_scattermore()`).\"\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[1] \"Saving to file 20094_0001_scatter_ncount_rp.pdf\"\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Warning message:\n", "\"\u001b[1m\u001b[22mRemoved 5852225 rows containing missing values (`geom_scattermore()`).\"\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[1] \"Saving to file 20094_0001_scatter_ncount_rp.svg\"\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Warning message:\n", "\"\u001b[1m\u001b[22mRemoved 5852225 rows containing missing values (`geom_scattermore()`).\"\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[1] \"Saving to file 20094_0001_scatter_ncount_rp.data\"\n", "[1] \"20094_0002\"\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Rasterizing points since number of points exceeds 100,000.\n", "To disable this behavior set `raster=FALSE`\n", "\n", "Rasterizing points since number of points exceeds 100,000.\n", "To disable this behavior set `raster=FALSE`\n", "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[1] \"20094_0002_scatter_ncount_mt 10 6\"\n", "[1] \"Saving to file 20094_0002_scatter_ncount_mt.png\"\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Warning message:\n", "\"\u001b[1m\u001b[22mRemoved 5792141 rows containing missing values (`geom_scattermore()`).\"\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[1] \"Saving to file 20094_0002_scatter_ncount_mt.pdf\"\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Warning message:\n", "\"\u001b[1m\u001b[22mRemoved 5792141 rows containing missing values (`geom_scattermore()`).\"\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[1] \"Saving to file 20094_0002_scatter_ncount_mt.svg\"\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Warning message:\n", "\"\u001b[1m\u001b[22mRemoved 5792141 rows containing missing values (`geom_scattermore()`).\"\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[1] \"Saving to file 20094_0002_scatter_ncount_mt.data\"\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Rasterizing points since number of points exceeds 100,000.\n", "To disable this behavior set `raster=FALSE`\n", "\n", "Rasterizing points since number of points exceeds 100,000.\n", "To disable this behavior set `raster=FALSE`\n", "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[1] \"20094_0002_scatter_ncount_rp 10 6\"\n", "[1] \"Saving to file 20094_0002_scatter_ncount_rp.png\"\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Warning message:\n", "\"\u001b[1m\u001b[22mRemoved 5792141 rows containing missing values (`geom_scattermore()`).\"\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[1] \"Saving to file 20094_0002_scatter_ncount_rp.pdf\"\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Warning message:\n", "\"\u001b[1m\u001b[22mRemoved 5792141 rows containing missing values (`geom_scattermore()`).\"\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[1] \"Saving to file 20094_0002_scatter_ncount_rp.svg\"\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Warning message:\n", "\"\u001b[1m\u001b[22mRemoved 5792141 rows containing missing values (`geom_scattermore()`).\"\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[1] \"Saving to file 20094_0002_scatter_ncount_rp.data\"\n", "[1] \"20094_0003\"\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Rasterizing points since number of points exceeds 100,000.\n", "To disable this behavior set `raster=FALSE`\n", "\n", "Rasterizing points since number of points exceeds 100,000.\n", "To disable this behavior set `raster=FALSE`\n", "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[1] \"20094_0003_scatter_ncount_mt 10 6\"\n", "[1] \"Saving to file 20094_0003_scatter_ncount_mt.png\"\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Warning message:\n", "\"\u001b[1m\u001b[22mRemoved 5903932 rows containing missing values (`geom_scattermore()`).\"\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[1] \"Saving to file 20094_0003_scatter_ncount_mt.pdf\"\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Warning message:\n", "\"\u001b[1m\u001b[22mRemoved 5903932 rows containing missing values (`geom_scattermore()`).\"\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[1] \"Saving to file 20094_0003_scatter_ncount_mt.svg\"\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Warning message:\n", "\"\u001b[1m\u001b[22mRemoved 5903932 rows containing missing values (`geom_scattermore()`).\"\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[1] \"Saving to file 20094_0003_scatter_ncount_mt.data\"\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Rasterizing points since number of points exceeds 100,000.\n", "To disable this behavior set `raster=FALSE`\n", "\n", "Rasterizing points since number of points exceeds 100,000.\n", "To disable this behavior set `raster=FALSE`\n", "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[1] \"20094_0003_scatter_ncount_rp 10 6\"\n", "[1] \"Saving to file 20094_0003_scatter_ncount_rp.png\"\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Warning message:\n", "\"\u001b[1m\u001b[22mRemoved 5903932 rows containing missing values (`geom_scattermore()`).\"\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[1] \"Saving to file 20094_0003_scatter_ncount_rp.pdf\"\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Warning message:\n", "\"\u001b[1m\u001b[22mRemoved 5903932 rows containing missing values (`geom_scattermore()`).\"\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[1] \"Saving to file 20094_0003_scatter_ncount_rp.svg\"\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Warning message:\n", "\"\u001b[1m\u001b[22mRemoved 5903932 rows containing missing values (`geom_scattermore()`).\"\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[1] \"Saving to file 20094_0003_scatter_ncount_rp.data\"\n", "[1] \"20094_0004\"\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Rasterizing points since number of points exceeds 100,000.\n", "To disable this behavior set `raster=FALSE`\n", "\n", "Rasterizing points since number of points exceeds 100,000.\n", "To disable this behavior set `raster=FALSE`\n", "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[1] \"20094_0004_scatter_ncount_mt 10 6\"\n", "[1] \"Saving to file 20094_0004_scatter_ncount_mt.png\"\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Warning message:\n", "\"\u001b[1m\u001b[22mRemoved 6030651 rows containing missing values (`geom_scattermore()`).\"\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[1] \"Saving to file 20094_0004_scatter_ncount_mt.pdf\"\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Warning message:\n", "\"\u001b[1m\u001b[22mRemoved 6030651 rows containing missing values (`geom_scattermore()`).\"\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[1] \"Saving to file 20094_0004_scatter_ncount_mt.svg\"\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Warning message:\n", "\"\u001b[1m\u001b[22mRemoved 6030651 rows containing missing values (`geom_scattermore()`).\"\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[1] \"Saving to file 20094_0004_scatter_ncount_mt.data\"\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Rasterizing points since number of points exceeds 100,000.\n", "To disable this behavior set `raster=FALSE`\n", "\n", "Rasterizing points since number of points exceeds 100,000.\n", "To disable this behavior set `raster=FALSE`\n", "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[1] \"20094_0004_scatter_ncount_rp 10 6\"\n", "[1] \"Saving to file 20094_0004_scatter_ncount_rp.png\"\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Warning message:\n", "\"\u001b[1m\u001b[22mRemoved 6030651 rows containing missing values (`geom_scattermore()`).\"\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[1] \"Saving to file 20094_0004_scatter_ncount_rp.pdf\"\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Warning message:\n", "\"\u001b[1m\u001b[22mRemoved 6030651 rows containing missing values (`geom_scattermore()`).\"\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[1] \"Saving to file 20094_0004_scatter_ncount_rp.svg\"\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Warning message:\n", "\"\u001b[1m\u001b[22mRemoved 6030651 rows containing missing values (`geom_scattermore()`).\"\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[1] \"Saving to file 20094_0004_scatter_ncount_rp.data\"\n", "[1] \"20094_0005\"\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Rasterizing points since number of points exceeds 100,000.\n", "To disable this behavior set `raster=FALSE`\n", "\n", "Rasterizing points since number of points exceeds 100,000.\n", "To disable this behavior set `raster=FALSE`\n", "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[1] \"20094_0005_scatter_ncount_mt 10 6\"\n", "[1] \"Saving to file 20094_0005_scatter_ncount_mt.png\"\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Warning message:\n", "\"\u001b[1m\u001b[22mRemoved 5969738 rows containing missing values (`geom_scattermore()`).\"\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[1] \"Saving to file 20094_0005_scatter_ncount_mt.pdf\"\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Warning message:\n", "\"\u001b[1m\u001b[22mRemoved 5969738 rows containing missing values (`geom_scattermore()`).\"\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[1] \"Saving to file 20094_0005_scatter_ncount_mt.svg\"\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Warning message:\n", "\"\u001b[1m\u001b[22mRemoved 5969738 rows containing missing values (`geom_scattermore()`).\"\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[1] \"Saving to file 20094_0005_scatter_ncount_mt.data\"\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Rasterizing points since number of points exceeds 100,000.\n", "To disable this behavior set `raster=FALSE`\n", "\n", "Rasterizing points since number of points exceeds 100,000.\n", "To disable this behavior set `raster=FALSE`\n", "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[1] \"20094_0005_scatter_ncount_rp 10 6\"\n", "[1] \"Saving to file 20094_0005_scatter_ncount_rp.png\"\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Warning message:\n", "\"\u001b[1m\u001b[22mRemoved 5969738 rows containing missing values (`geom_scattermore()`).\"\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[1] \"Saving to file 20094_0005_scatter_ncount_rp.pdf\"\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Warning message:\n", "\"\u001b[1m\u001b[22mRemoved 5969738 rows containing missing values (`geom_scattermore()`).\"\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[1] \"Saving to file 20094_0005_scatter_ncount_rp.svg\"\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Warning message:\n", "\"\u001b[1m\u001b[22mRemoved 5969738 rows containing missing values (`geom_scattermore()`).\"\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[1] \"Saving to file 20094_0005_scatter_ncount_rp.data\"\n", "[1] \"20094_0006\"\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Rasterizing points since number of points exceeds 100,000.\n", "To disable this behavior set `raster=FALSE`\n", "\n", "Rasterizing points since number of points exceeds 100,000.\n", "To disable this behavior set `raster=FALSE`\n", "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[1] \"20094_0006_scatter_ncount_mt 10 6\"\n", "[1] \"Saving to file 20094_0006_scatter_ncount_mt.png\"\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Warning message:\n", "\"\u001b[1m\u001b[22mRemoved 6142271 rows containing missing values (`geom_scattermore()`).\"\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[1] \"Saving to file 20094_0006_scatter_ncount_mt.pdf\"\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Warning message:\n", "\"\u001b[1m\u001b[22mRemoved 6142271 rows containing missing values (`geom_scattermore()`).\"\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[1] \"Saving to file 20094_0006_scatter_ncount_mt.svg\"\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Warning message:\n", "\"\u001b[1m\u001b[22mRemoved 6142271 rows containing missing values (`geom_scattermore()`).\"\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[1] \"Saving to file 20094_0006_scatter_ncount_mt.data\"\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Rasterizing points since number of points exceeds 100,000.\n", "To disable this behavior set `raster=FALSE`\n", "\n", "Rasterizing points since number of points exceeds 100,000.\n", "To disable this behavior set `raster=FALSE`\n", "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[1] \"20094_0006_scatter_ncount_rp 10 6\"\n", "[1] \"Saving to file 20094_0006_scatter_ncount_rp.png\"\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Warning message:\n", "\"\u001b[1m\u001b[22mRemoved 6142271 rows containing missing values (`geom_scattermore()`).\"\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[1] \"Saving to file 20094_0006_scatter_ncount_rp.pdf\"\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Warning message:\n", "\"\u001b[1m\u001b[22mRemoved 6142271 rows containing missing values (`geom_scattermore()`).\"\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[1] \"Saving to file 20094_0006_scatter_ncount_rp.svg\"\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Warning message:\n", "\"\u001b[1m\u001b[22mRemoved 6142271 rows containing missing values (`geom_scattermore()`).\"\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[1] \"Saving to file 20094_0006_scatter_ncount_rp.data\"\n", "[1] \"20094_0007\"\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Rasterizing points since number of points exceeds 100,000.\n", "To disable this behavior set `raster=FALSE`\n", "\n", "Rasterizing points since number of points exceeds 100,000.\n", "To disable this behavior set `raster=FALSE`\n", "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[1] \"20094_0007_scatter_ncount_mt 10 6\"\n", "[1] \"Saving to file 20094_0007_scatter_ncount_mt.png\"\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Warning message:\n", "\"\u001b[1m\u001b[22mRemoved 6165792 rows containing missing values (`geom_scattermore()`).\"\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[1] \"Saving to file 20094_0007_scatter_ncount_mt.pdf\"\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Warning message:\n", "\"\u001b[1m\u001b[22mRemoved 6165792 rows containing missing values (`geom_scattermore()`).\"\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[1] \"Saving to file 20094_0007_scatter_ncount_mt.svg\"\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Warning message:\n", "\"\u001b[1m\u001b[22mRemoved 6165792 rows containing missing values (`geom_scattermore()`).\"\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[1] \"Saving to file 20094_0007_scatter_ncount_mt.data\"\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Rasterizing points since number of points exceeds 100,000.\n", "To disable this behavior set `raster=FALSE`\n", "\n", "Rasterizing points since number of points exceeds 100,000.\n", "To disable this behavior set `raster=FALSE`\n", "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[1] \"20094_0007_scatter_ncount_rp 10 6\"\n", "[1] \"Saving to file 20094_0007_scatter_ncount_rp.png\"\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Warning message:\n", "\"\u001b[1m\u001b[22mRemoved 6165792 rows containing missing values (`geom_scattermore()`).\"\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[1] \"Saving to file 20094_0007_scatter_ncount_rp.pdf\"\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Warning message:\n", "\"\u001b[1m\u001b[22mRemoved 6165792 rows containing missing values (`geom_scattermore()`).\"\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[1] \"Saving to file 20094_0007_scatter_ncount_rp.svg\"\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Warning message:\n", "\"\u001b[1m\u001b[22mRemoved 6165792 rows containing missing values (`geom_scattermore()`).\"\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[1] \"Saving to file 20094_0007_scatter_ncount_rp.data\"\n", "[1] \"20094_0008\"\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Rasterizing points since number of points exceeds 100,000.\n", "To disable this behavior set `raster=FALSE`\n", "\n", "Rasterizing points since number of points exceeds 100,000.\n", "To disable this behavior set `raster=FALSE`\n", "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[1] \"20094_0008_scatter_ncount_mt 10 6\"\n", "[1] \"Saving to file 20094_0008_scatter_ncount_mt.png\"\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Warning message:\n", "\"\u001b[1m\u001b[22mRemoved 5878998 rows containing missing values (`geom_scattermore()`).\"\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[1] \"Saving to file 20094_0008_scatter_ncount_mt.pdf\"\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Warning message:\n", "\"\u001b[1m\u001b[22mRemoved 5878998 rows containing missing values (`geom_scattermore()`).\"\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[1] \"Saving to file 20094_0008_scatter_ncount_mt.svg\"\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Warning message:\n", "\"\u001b[1m\u001b[22mRemoved 5878998 rows containing missing values (`geom_scattermore()`).\"\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[1] \"Saving to file 20094_0008_scatter_ncount_mt.data\"\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Rasterizing points since number of points exceeds 100,000.\n", "To disable this behavior set `raster=FALSE`\n", "\n", "Rasterizing points since number of points exceeds 100,000.\n", "To disable this behavior set `raster=FALSE`\n", "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[1] \"20094_0008_scatter_ncount_rp 10 6\"\n", "[1] \"Saving to file 20094_0008_scatter_ncount_rp.png\"\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Warning message:\n", "\"\u001b[1m\u001b[22mRemoved 5878998 rows containing missing values (`geom_scattermore()`).\"\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[1] \"Saving to file 20094_0008_scatter_ncount_rp.pdf\"\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Warning message:\n", "\"\u001b[1m\u001b[22mRemoved 5878998 rows containing missing values (`geom_scattermore()`).\"\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[1] \"Saving to file 20094_0008_scatter_ncount_rp.svg\"\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Warning message:\n", "\"\u001b[1m\u001b[22mRemoved 5878998 rows containing missing values (`geom_scattermore()`).\"\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[1] \"Saving to file 20094_0008_scatter_ncount_rp.data\"\n", "[1] \"20094_0009\"\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Rasterizing points since number of points exceeds 100,000.\n", "To disable this behavior set `raster=FALSE`\n", "\n", "Rasterizing points since number of points exceeds 100,000.\n", "To disable this behavior set `raster=FALSE`\n", "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[1] \"20094_0009_scatter_ncount_mt 10 6\"\n", "[1] \"Saving to file 20094_0009_scatter_ncount_mt.png\"\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Warning message:\n", "\"\u001b[1m\u001b[22mRemoved 6065598 rows containing missing values (`geom_scattermore()`).\"\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[1] \"Saving to file 20094_0009_scatter_ncount_mt.pdf\"\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Warning message:\n", "\"\u001b[1m\u001b[22mRemoved 6065598 rows containing missing values (`geom_scattermore()`).\"\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[1] \"Saving to file 20094_0009_scatter_ncount_mt.svg\"\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Warning message:\n", "\"\u001b[1m\u001b[22mRemoved 6065598 rows containing missing values (`geom_scattermore()`).\"\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[1] \"Saving to file 20094_0009_scatter_ncount_mt.data\"\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Rasterizing points since number of points exceeds 100,000.\n", "To disable this behavior set `raster=FALSE`\n", "\n", "Rasterizing points since number of points exceeds 100,000.\n", "To disable this behavior set `raster=FALSE`\n", "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[1] \"20094_0009_scatter_ncount_rp 10 6\"\n", "[1] \"Saving to file 20094_0009_scatter_ncount_rp.png\"\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Warning message:\n", "\"\u001b[1m\u001b[22mRemoved 6065598 rows containing missing values (`geom_scattermore()`).\"\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[1] \"Saving to file 20094_0009_scatter_ncount_rp.pdf\"\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Warning message:\n", "\"\u001b[1m\u001b[22mRemoved 6065598 rows containing missing values (`geom_scattermore()`).\"\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[1] \"Saving to file 20094_0009_scatter_ncount_rp.svg\"\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Warning message:\n", "\"\u001b[1m\u001b[22mRemoved 6065598 rows containing missing values (`geom_scattermore()`).\"\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[1] \"Saving to file 20094_0009_scatter_ncount_rp.data\"\n", "[1] \"20094_0012\"\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Rasterizing points since number of points exceeds 100,000.\n", "To disable this behavior set `raster=FALSE`\n", "\n", "Rasterizing points since number of points exceeds 100,000.\n", "To disable this behavior set `raster=FALSE`\n", "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[1] \"20094_0012_scatter_ncount_mt 10 6\"\n", "[1] \"Saving to file 20094_0012_scatter_ncount_mt.png\"\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Warning message:\n", "\"\u001b[1m\u001b[22mRemoved 5739880 rows containing missing values (`geom_scattermore()`).\"\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[1] \"Saving to file 20094_0012_scatter_ncount_mt.pdf\"\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Warning message:\n", "\"\u001b[1m\u001b[22mRemoved 5739880 rows containing missing values (`geom_scattermore()`).\"\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[1] \"Saving to file 20094_0012_scatter_ncount_mt.svg\"\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Warning message:\n", "\"\u001b[1m\u001b[22mRemoved 5739880 rows containing missing values (`geom_scattermore()`).\"\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[1] \"Saving to file 20094_0012_scatter_ncount_mt.data\"\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Rasterizing points since number of points exceeds 100,000.\n", "To disable this behavior set `raster=FALSE`\n", "\n", "Rasterizing points since number of points exceeds 100,000.\n", "To disable this behavior set `raster=FALSE`\n", "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[1] \"20094_0012_scatter_ncount_rp 10 6\"\n", "[1] \"Saving to file 20094_0012_scatter_ncount_rp.png\"\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Warning message:\n", "\"\u001b[1m\u001b[22mRemoved 5739880 rows containing missing values (`geom_scattermore()`).\"\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[1] \"Saving to file 20094_0012_scatter_ncount_rp.pdf\"\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Warning message:\n", "\"\u001b[1m\u001b[22mRemoved 5739880 rows containing missing values (`geom_scattermore()`).\"\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[1] \"Saving to file 20094_0012_scatter_ncount_rp.svg\"\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Warning message:\n", "\"\u001b[1m\u001b[22mRemoved 5739880 rows containing missing values (`geom_scattermore()`).\"\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[1] \"Saving to file 20094_0012_scatter_ncount_rp.data\"\n", "An object of class Seurat \n", "36601 features across 2534 samples within 1 assay \n", "Active assay: RNA (36601 features, 3000 variable features)\n", "An object of class Seurat \n", "36601 features across 2143 samples within 1 assay \n", "Active assay: RNA (36601 features, 3000 variable features)\n", "An object of class Seurat \n", "36601 features across 1969 samples within 1 assay \n", "Active assay: RNA (36601 features, 3000 variable features)\n", "An object of class Seurat \n", "36601 features across 884 samples within 1 assay \n", "Active assay: RNA (36601 features, 3000 variable features)\n", "An object of class Seurat \n", "36601 features across 1202 samples within 1 assay \n", "Active assay: RNA (36601 features, 3000 variable features)\n", "An object of class Seurat \n", "36601 features across 198 samples within 1 assay \n", "Active assay: RNA (36601 features, 3000 variable features)\n", "An object of class Seurat \n", "36601 features across 490 samples within 1 assay \n", "Active assay: RNA (36601 features, 3000 variable features)\n", "An object of class Seurat \n", "36601 features across 1037 samples within 1 assay \n", "Active assay: RNA (36601 features, 3000 variable features)\n", "An object of class Seurat \n", "36601 features across 766 samples within 1 assay \n", "Active assay: RNA (36601 features, 3000 variable features)\n", "An object of class Seurat \n", "36601 features across 3449 samples within 1 assay \n", "Active assay: RNA (36601 features, 3000 variable features)\n", "[1] \"20094_0001_filtered_violins_qc 10 6\"\n", "[1] \"Saving to file 20094_0001_filtered_violins_qc.png\"\n", "[1] \"Saving to file 20094_0001_filtered_violins_qc.pdf\"\n", "[1] \"Saving to file 20094_0001_filtered_violins_qc.svg\"\n", "[1] \"Saving to file 20094_0001_filtered_violins_qc.data\"\n", "[1] \"20094_0001_filtered_scatter_ncount_mt 10 6\"\n", "[1] \"Saving to file 20094_0001_filtered_scatter_ncount_mt.png\"\n", "[1] \"Saving to file 20094_0001_filtered_scatter_ncount_mt.pdf\"\n", "[1] \"Saving to file 20094_0001_filtered_scatter_ncount_mt.svg\"\n", "[1] \"Saving to file 20094_0001_filtered_scatter_ncount_mt.data\"\n", "[1] \"20094_0001_filtered_scatter_ncount_rp 10 6\"\n", "[1] \"Saving to file 20094_0001_filtered_scatter_ncount_rp.png\"\n", "[1] \"Saving to file 20094_0001_filtered_scatter_ncount_rp.pdf\"\n", "[1] \"Saving to file 20094_0001_filtered_scatter_ncount_rp.svg\"\n", "[1] \"Saving to file 20094_0001_filtered_scatter_ncount_rp.data\"\n", "[1] \"20094_0002_filtered_violins_qc 10 6\"\n", "[1] \"Saving to file 20094_0002_filtered_violins_qc.png\"\n", "[1] \"Saving to file 20094_0002_filtered_violins_qc.pdf\"\n", "[1] \"Saving to file 20094_0002_filtered_violins_qc.svg\"\n", "[1] \"Saving to file 20094_0002_filtered_violins_qc.data\"\n", "[1] \"20094_0002_filtered_scatter_ncount_mt 10 6\"\n", "[1] \"Saving to file 20094_0002_filtered_scatter_ncount_mt.png\"\n", "[1] \"Saving to file 20094_0002_filtered_scatter_ncount_mt.pdf\"\n", "[1] \"Saving to file 20094_0002_filtered_scatter_ncount_mt.svg\"\n", "[1] \"Saving to file 20094_0002_filtered_scatter_ncount_mt.data\"\n", "[1] \"20094_0002_filtered_scatter_ncount_rp 10 6\"\n", "[1] \"Saving to file 20094_0002_filtered_scatter_ncount_rp.png\"\n", "[1] \"Saving to file 20094_0002_filtered_scatter_ncount_rp.pdf\"\n", "[1] \"Saving to file 20094_0002_filtered_scatter_ncount_rp.svg\"\n", "[1] \"Saving to file 20094_0002_filtered_scatter_ncount_rp.data\"\n", "[1] \"20094_0003_filtered_violins_qc 10 6\"\n", "[1] \"Saving to file 20094_0003_filtered_violins_qc.png\"\n", "[1] \"Saving to file 20094_0003_filtered_violins_qc.pdf\"\n", "[1] \"Saving to file 20094_0003_filtered_violins_qc.svg\"\n", "[1] \"Saving to file 20094_0003_filtered_violins_qc.data\"\n", "[1] \"20094_0003_filtered_scatter_ncount_mt 10 6\"\n", "[1] \"Saving to file 20094_0003_filtered_scatter_ncount_mt.png\"\n", "[1] \"Saving to file 20094_0003_filtered_scatter_ncount_mt.pdf\"\n", "[1] \"Saving to file 20094_0003_filtered_scatter_ncount_mt.svg\"\n", "[1] \"Saving to file 20094_0003_filtered_scatter_ncount_mt.data\"\n", "[1] \"20094_0003_filtered_scatter_ncount_rp 10 6\"\n", "[1] \"Saving to file 20094_0003_filtered_scatter_ncount_rp.png\"\n", "[1] \"Saving to file 20094_0003_filtered_scatter_ncount_rp.pdf\"\n", "[1] \"Saving to file 20094_0003_filtered_scatter_ncount_rp.svg\"\n", "[1] \"Saving to file 20094_0003_filtered_scatter_ncount_rp.data\"\n", "[1] \"20094_0004_filtered_violins_qc 10 6\"\n", "[1] \"Saving to file 20094_0004_filtered_violins_qc.png\"\n", "[1] \"Saving to file 20094_0004_filtered_violins_qc.pdf\"\n", "[1] \"Saving to file 20094_0004_filtered_violins_qc.svg\"\n", "[1] \"Saving to file 20094_0004_filtered_violins_qc.data\"\n", "[1] \"20094_0004_filtered_scatter_ncount_mt 10 6\"\n", "[1] \"Saving to file 20094_0004_filtered_scatter_ncount_mt.png\"\n", "[1] \"Saving to file 20094_0004_filtered_scatter_ncount_mt.pdf\"\n", "[1] \"Saving to file 20094_0004_filtered_scatter_ncount_mt.svg\"\n", "[1] \"Saving to file 20094_0004_filtered_scatter_ncount_mt.data\"\n", "[1] \"20094_0004_filtered_scatter_ncount_rp 10 6\"\n", "[1] \"Saving to file 20094_0004_filtered_scatter_ncount_rp.png\"\n", "[1] \"Saving to file 20094_0004_filtered_scatter_ncount_rp.pdf\"\n", "[1] \"Saving to file 20094_0004_filtered_scatter_ncount_rp.svg\"\n", "[1] \"Saving to file 20094_0004_filtered_scatter_ncount_rp.data\"\n", "[1] \"20094_0005_filtered_violins_qc 10 6\"\n", "[1] \"Saving to file 20094_0005_filtered_violins_qc.png\"\n", "[1] \"Saving to file 20094_0005_filtered_violins_qc.pdf\"\n", "[1] \"Saving to file 20094_0005_filtered_violins_qc.svg\"\n", "[1] \"Saving to file 20094_0005_filtered_violins_qc.data\"\n", "[1] \"20094_0005_filtered_scatter_ncount_mt 10 6\"\n", "[1] \"Saving to file 20094_0005_filtered_scatter_ncount_mt.png\"\n", "[1] \"Saving to file 20094_0005_filtered_scatter_ncount_mt.pdf\"\n", "[1] \"Saving to file 20094_0005_filtered_scatter_ncount_mt.svg\"\n", "[1] \"Saving to file 20094_0005_filtered_scatter_ncount_mt.data\"\n", "[1] \"20094_0005_filtered_scatter_ncount_rp 10 6\"\n", "[1] \"Saving to file 20094_0005_filtered_scatter_ncount_rp.png\"\n", "[1] \"Saving to file 20094_0005_filtered_scatter_ncount_rp.pdf\"\n", "[1] \"Saving to file 20094_0005_filtered_scatter_ncount_rp.svg\"\n", "[1] \"Saving to file 20094_0005_filtered_scatter_ncount_rp.data\"\n", "[1] \"20094_0006_filtered_violins_qc 10 6\"\n", "[1] \"Saving to file 20094_0006_filtered_violins_qc.png\"\n", "[1] \"Saving to file 20094_0006_filtered_violins_qc.pdf\"\n", "[1] \"Saving to file 20094_0006_filtered_violins_qc.svg\"\n", "[1] \"Saving to file 20094_0006_filtered_violins_qc.data\"\n", "[1] \"20094_0006_filtered_scatter_ncount_mt 10 6\"\n", "[1] \"Saving to file 20094_0006_filtered_scatter_ncount_mt.png\"\n", "[1] \"Saving to file 20094_0006_filtered_scatter_ncount_mt.pdf\"\n", "[1] \"Saving to file 20094_0006_filtered_scatter_ncount_mt.svg\"\n", "[1] \"Saving to file 20094_0006_filtered_scatter_ncount_mt.data\"\n", "[1] \"20094_0006_filtered_scatter_ncount_rp 10 6\"\n", "[1] \"Saving to file 20094_0006_filtered_scatter_ncount_rp.png\"\n", "[1] \"Saving to file 20094_0006_filtered_scatter_ncount_rp.pdf\"\n", "[1] \"Saving to file 20094_0006_filtered_scatter_ncount_rp.svg\"\n", "[1] \"Saving to file 20094_0006_filtered_scatter_ncount_rp.data\"\n", "[1] \"20094_0007_filtered_violins_qc 10 6\"\n", "[1] \"Saving to file 20094_0007_filtered_violins_qc.png\"\n", "[1] \"Saving to file 20094_0007_filtered_violins_qc.pdf\"\n", "[1] \"Saving to file 20094_0007_filtered_violins_qc.svg\"\n", "[1] \"Saving to file 20094_0007_filtered_violins_qc.data\"\n", "[1] \"20094_0007_filtered_scatter_ncount_mt 10 6\"\n", "[1] \"Saving to file 20094_0007_filtered_scatter_ncount_mt.png\"\n", "[1] \"Saving to file 20094_0007_filtered_scatter_ncount_mt.pdf\"\n", "[1] \"Saving to file 20094_0007_filtered_scatter_ncount_mt.svg\"\n", "[1] \"Saving to file 20094_0007_filtered_scatter_ncount_mt.data\"\n", "[1] \"20094_0007_filtered_scatter_ncount_rp 10 6\"\n", "[1] \"Saving to file 20094_0007_filtered_scatter_ncount_rp.png\"\n", "[1] \"Saving to file 20094_0007_filtered_scatter_ncount_rp.pdf\"\n", "[1] \"Saving to file 20094_0007_filtered_scatter_ncount_rp.svg\"\n", "[1] \"Saving to file 20094_0007_filtered_scatter_ncount_rp.data\"\n", "[1] \"20094_0008_filtered_violins_qc 10 6\"\n", "[1] \"Saving to file 20094_0008_filtered_violins_qc.png\"\n", "[1] \"Saving to file 20094_0008_filtered_violins_qc.pdf\"\n", "[1] \"Saving to file 20094_0008_filtered_violins_qc.svg\"\n", "[1] \"Saving to file 20094_0008_filtered_violins_qc.data\"\n", "[1] \"20094_0008_filtered_scatter_ncount_mt 10 6\"\n", "[1] \"Saving to file 20094_0008_filtered_scatter_ncount_mt.png\"\n", "[1] \"Saving to file 20094_0008_filtered_scatter_ncount_mt.pdf\"\n", "[1] \"Saving to file 20094_0008_filtered_scatter_ncount_mt.svg\"\n", "[1] \"Saving to file 20094_0008_filtered_scatter_ncount_mt.data\"\n", "[1] \"20094_0008_filtered_scatter_ncount_rp 10 6\"\n", "[1] \"Saving to file 20094_0008_filtered_scatter_ncount_rp.png\"\n", "[1] \"Saving to file 20094_0008_filtered_scatter_ncount_rp.pdf\"\n", "[1] \"Saving to file 20094_0008_filtered_scatter_ncount_rp.svg\"\n", "[1] \"Saving to file 20094_0008_filtered_scatter_ncount_rp.data\"\n", "[1] \"20094_0009_filtered_violins_qc 10 6\"\n", "[1] \"Saving to file 20094_0009_filtered_violins_qc.png\"\n", "[1] \"Saving to file 20094_0009_filtered_violins_qc.pdf\"\n", "[1] \"Saving to file 20094_0009_filtered_violins_qc.svg\"\n", "[1] \"Saving to file 20094_0009_filtered_violins_qc.data\"\n", "[1] \"20094_0009_filtered_scatter_ncount_mt 10 6\"\n", "[1] \"Saving to file 20094_0009_filtered_scatter_ncount_mt.png\"\n", "[1] \"Saving to file 20094_0009_filtered_scatter_ncount_mt.pdf\"\n", "[1] \"Saving to file 20094_0009_filtered_scatter_ncount_mt.svg\"\n", "[1] \"Saving to file 20094_0009_filtered_scatter_ncount_mt.data\"\n", "[1] \"20094_0009_filtered_scatter_ncount_rp 10 6\"\n", "[1] \"Saving to file 20094_0009_filtered_scatter_ncount_rp.png\"\n", "[1] \"Saving to file 20094_0009_filtered_scatter_ncount_rp.pdf\"\n", "[1] \"Saving to file 20094_0009_filtered_scatter_ncount_rp.svg\"\n", "[1] \"Saving to file 20094_0009_filtered_scatter_ncount_rp.data\"\n", "[1] \"20094_0012_filtered_violins_qc 10 6\"\n", "[1] \"Saving to file 20094_0012_filtered_violins_qc.png\"\n", "[1] \"Saving to file 20094_0012_filtered_violins_qc.pdf\"\n", "[1] \"Saving to file 20094_0012_filtered_violins_qc.svg\"\n", "[1] \"Saving to file 20094_0012_filtered_violins_qc.data\"\n", "[1] \"20094_0012_filtered_scatter_ncount_mt 10 6\"\n", "[1] \"Saving to file 20094_0012_filtered_scatter_ncount_mt.png\"\n", "[1] \"Saving to file 20094_0012_filtered_scatter_ncount_mt.pdf\"\n", "[1] \"Saving to file 20094_0012_filtered_scatter_ncount_mt.svg\"\n", "[1] \"Saving to file 20094_0012_filtered_scatter_ncount_mt.data\"\n", "[1] \"20094_0012_filtered_scatter_ncount_rp 10 6\"\n", "[1] \"Saving to file 20094_0012_filtered_scatter_ncount_rp.png\"\n", "[1] \"Saving to file 20094_0012_filtered_scatter_ncount_rp.pdf\"\n", "[1] \"Saving to file 20094_0012_filtered_scatter_ncount_rp.svg\"\n", "[1] \"Saving to file 20094_0012_filtered_scatter_ncount_rp.data\"\n" ] } ], "source": [ "\n", "objlist = scatterAndFilter(objlist.raw, nfeature_rna.lower=100, nfeature_rna.upper=6000, ncount_rna.lower=500, percent_mt.upper=7)\n" ] }, { "cell_type": "code", "execution_count": 7, "id": "a5a35c3a", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[1] \"cells per experiment\"\n", "20094_0001 20094_0002 20094_0003 20094_0004 20094_0005 20094_0006 20094_0007 \n", " 2534 2143 1969 884 1202 198 490 \n", "20094_0008 20094_0009 20094_0012 \n", " 1037 766 3449 \n", "[1] \"total cells\"\n", "[1] 14672\n" ] } ], "source": [ "\n", "print(\"cells per experiment\")\n", "print(mapply(sum, lapply(objlist, function(x) {dim(x)[2]})))\n", "print(\"total cells\")\n", "print(sum(mapply(sum, lapply(objlist, function(x) {dim(x)[2]}))))\n", "\n" ] }, { "cell_type": "code", "execution_count": 8, "id": "9656c790", "metadata": {}, "outputs": [], "source": [ "\n", "objlist.raw = NULL\n", "inputMatrices = NULL\n" ] }, { "cell_type": "code", "execution_count": 9, "id": "90e0d87a", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[1] \"cells per experiment\"\n", "20094_0001 20094_0002 20094_0003 20094_0004 20094_0005 20094_0006 20094_0007 \n", " 2534 2143 1969 884 1202 198 490 \n", "20094_0008 20094_0009 20094_0012 \n", " 1037 766 3449 \n", "[1] \"total cells\"\n", "[1] 14672\n", "[1] \"Seurat obj project 20094_0001\"\n", "[1] \"Seurat obj project 20094_0002\"\n", "[1] \"Seurat obj project 20094_0003\"\n", "[1] \"Seurat obj project 20094_0004\"\n", "[1] \"Seurat obj project 20094_0005\"\n", "[1] \"Seurat obj project 20094_0006\"\n", "[1] \"Seurat obj project 20094_0007\"\n", "[1] \"Seurat obj project 20094_0008\"\n", "[1] \"Seurat obj project 20094_0009\"\n", "[1] \"Seurat obj project 20094_0012\"\n", "[1] \"SelectIntegrationFeatures\"\n", "[1] \"Seurat obj project 20094_0001\"\n", "An object of class Seurat \n", "36601 features across 2534 samples within 1 assay \n", "Active assay: RNA (36601 features, 3000 variable features)\n", "[1] \"CellCycle 42 52\"\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Regressing out percent.rp, percent.mt, nCount_RNA, S.Score, G2M.Score\n", "\n", "Centering and scaling data matrix\n", "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[1] \"Seurat obj project 20094_0002\"\n", "An object of class Seurat \n", "36601 features across 2143 samples within 1 assay \n", "Active assay: RNA (36601 features, 3000 variable features)\n", "[1] \"CellCycle 42 52\"\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Regressing out percent.rp, percent.mt, nCount_RNA, S.Score, G2M.Score\n", "\n", "Centering and scaling data matrix\n", "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[1] \"Seurat obj project 20094_0003\"\n", "An object of class Seurat \n", "36601 features across 1969 samples within 1 assay \n", "Active assay: RNA (36601 features, 3000 variable features)\n", "[1] \"CellCycle 42 52\"\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Regressing out percent.rp, percent.mt, nCount_RNA, S.Score, G2M.Score\n", "\n", "Centering and scaling data matrix\n", "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[1] \"Seurat obj project 20094_0004\"\n", "An object of class Seurat \n", "36601 features across 884 samples within 1 assay \n", "Active assay: RNA (36601 features, 3000 variable features)\n", "[1] \"CellCycle 42 52\"\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Regressing out percent.rp, percent.mt, nCount_RNA, S.Score, G2M.Score\n", "\n", "Centering and scaling data matrix\n", "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[1] \"Seurat obj project 20094_0005\"\n", "An object of class Seurat \n", "36601 features across 1202 samples within 1 assay \n", "Active assay: RNA (36601 features, 3000 variable features)\n", "[1] \"CellCycle 42 52\"\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Regressing out percent.rp, percent.mt, nCount_RNA, S.Score, G2M.Score\n", "\n", "Centering and scaling data matrix\n", "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[1] \"Seurat obj project 20094_0006\"\n", "An object of class Seurat \n", "36601 features across 198 samples within 1 assay \n", "Active assay: RNA (36601 features, 3000 variable features)\n", "[1] \"CellCycle 42 52\"\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Regressing out percent.rp, percent.mt, nCount_RNA, S.Score, G2M.Score\n", "\n", "Centering and scaling data matrix\n", "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[1] \"Seurat obj project 20094_0007\"\n", "An object of class Seurat \n", "36601 features across 490 samples within 1 assay \n", "Active assay: RNA (36601 features, 3000 variable features)\n", "[1] \"CellCycle 42 52\"\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Regressing out percent.rp, percent.mt, nCount_RNA, S.Score, G2M.Score\n", "\n", "Centering and scaling data matrix\n", "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[1] \"Seurat obj project 20094_0008\"\n", "An object of class Seurat \n", "36601 features across 1037 samples within 1 assay \n", "Active assay: RNA (36601 features, 3000 variable features)\n", "[1] \"CellCycle 42 52\"\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Regressing out percent.rp, percent.mt, nCount_RNA, S.Score, G2M.Score\n", "\n", "Centering and scaling data matrix\n", "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[1] \"Seurat obj project 20094_0009\"\n", "An object of class Seurat \n", "36601 features across 766 samples within 1 assay \n", "Active assay: RNA (36601 features, 3000 variable features)\n", "[1] \"CellCycle 42 52\"\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Regressing out percent.rp, percent.mt, nCount_RNA, S.Score, G2M.Score\n", "\n", "Centering and scaling data matrix\n", "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[1] \"Seurat obj project 20094_0012\"\n", "An object of class Seurat \n", "36601 features across 3449 samples within 1 assay \n", "Active assay: RNA (36601 features, 3000 variable features)\n", "[1] \"CellCycle 42 52\"\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Regressing out percent.rp, percent.mt, nCount_RNA, S.Score, G2M.Score\n", "\n", "Centering and scaling data matrix\n", "\n" ] } ], "source": [ "\n", "finalList = prepareIntegration(objlist, cc.use.genes = cc.genes, nfeatures.variable = 3000, nfeatures.scale=3000, run.parallel=FALSE)\n" ] }, { "cell_type": "code", "execution_count": 10, "id": "8541c70e", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Warning message in dir.create(intname, recursive = TRUE):\n", "\"'libintegration' already exists\"\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "$`20094_0001`\n", "An object of class Seurat \n", "36601 features across 2534 samples within 1 assay \n", "Active assay: RNA (36601 features, 3000 variable features)\n", " 1 dimensional reduction calculated: pca\n", "\n", "$`20094_0002`\n", "An object of class Seurat \n", "36601 features across 2143 samples within 1 assay \n", "Active assay: RNA (36601 features, 3000 variable features)\n", " 1 dimensional reduction calculated: pca\n", "\n", "$`20094_0003`\n", "An object of class Seurat \n", "36601 features across 1969 samples within 1 assay \n", "Active assay: RNA (36601 features, 3000 variable features)\n", " 1 dimensional reduction calculated: pca\n", "\n", "$`20094_0004`\n", "An object of class Seurat \n", "36601 features across 884 samples within 1 assay \n", "Active assay: RNA (36601 features, 3000 variable features)\n", " 1 dimensional reduction calculated: pca\n", "\n", "$`20094_0005`\n", "An object of class Seurat \n", "36601 features across 1202 samples within 1 assay \n", "Active assay: RNA (36601 features, 3000 variable features)\n", " 1 dimensional reduction calculated: pca\n", "\n", "$`20094_0006`\n", "An object of class Seurat \n", "36601 features across 198 samples within 1 assay \n", "Active assay: RNA (36601 features, 3000 variable features)\n", " 1 dimensional reduction calculated: pca\n", "\n", "$`20094_0007`\n", "An object of class Seurat \n", "36601 features across 490 samples within 1 assay \n", "Active assay: RNA (36601 features, 3000 variable features)\n", " 1 dimensional reduction calculated: pca\n", "\n", "$`20094_0008`\n", "An object of class Seurat \n", "36601 features across 1037 samples within 1 assay \n", "Active assay: RNA (36601 features, 3000 variable features)\n", " 1 dimensional reduction calculated: pca\n", "\n", "$`20094_0009`\n", "An object of class Seurat \n", "36601 features across 766 samples within 1 assay \n", "Active assay: RNA (36601 features, 3000 variable features)\n", " 1 dimensional reduction calculated: pca\n", "\n", "$`20094_0012`\n", "An object of class Seurat \n", "36601 features across 3449 samples within 1 assay \n", "Active assay: RNA (36601 features, 3000 variable features)\n", " 1 dimensional reduction calculated: pca\n", "\n", "[1] \"GEX integration features\"\n", "$`20094_0001`\n", "An object of class Seurat \n", "36601 features across 2534 samples within 1 assay \n", "Active assay: RNA (36601 features, 3000 variable features)\n", " 1 dimensional reduction calculated: pca\n", "\n", "$`20094_0002`\n", "An object of class Seurat \n", "36601 features across 2143 samples within 1 assay \n", "Active assay: RNA (36601 features, 3000 variable features)\n", " 1 dimensional reduction calculated: pca\n", "\n", "$`20094_0003`\n", "An object of class Seurat \n", "36601 features across 1969 samples within 1 assay \n", "Active assay: RNA (36601 features, 3000 variable features)\n", " 1 dimensional reduction calculated: pca\n", "\n", "$`20094_0004`\n", "An object of class Seurat \n", "36601 features across 884 samples within 1 assay \n", "Active assay: RNA (36601 features, 3000 variable features)\n", " 1 dimensional reduction calculated: pca\n", "\n", "$`20094_0005`\n", "An object of class Seurat \n", "36601 features across 1202 samples within 1 assay \n", "Active assay: RNA (36601 features, 3000 variable features)\n", " 1 dimensional reduction calculated: pca\n", "\n", "$`20094_0006`\n", "An object of class Seurat \n", "36601 features across 198 samples within 1 assay \n", "Active assay: RNA (36601 features, 3000 variable features)\n", " 1 dimensional reduction calculated: pca\n", "\n", "$`20094_0007`\n", "An object of class Seurat \n", "36601 features across 490 samples within 1 assay \n", "Active assay: RNA (36601 features, 3000 variable features)\n", " 1 dimensional reduction calculated: pca\n", "\n", "$`20094_0008`\n", "An object of class Seurat \n", "36601 features across 1037 samples within 1 assay \n", "Active assay: RNA (36601 features, 3000 variable features)\n", " 1 dimensional reduction calculated: pca\n", "\n", "$`20094_0009`\n", "An object of class Seurat \n", "36601 features across 766 samples within 1 assay \n", "Active assay: RNA (36601 features, 3000 variable features)\n", " 1 dimensional reduction calculated: pca\n", "\n", "$`20094_0012`\n", "An object of class Seurat \n", "36601 features across 3449 samples within 1 assay \n", "Active assay: RNA (36601 features, 3000 variable features)\n", " 1 dimensional reduction calculated: pca\n", "\n", "[1] \"Current integration mode: rpca\"\n", "[1] \"Object 20094_0001\"\n", "[1] \"Object 20094_0002\"\n", "[1] \"Object 20094_0003\"\n", "[1] \"Object 20094_0004\"\n", "[1] \"Object 20094_0005\"\n", "[1] \"Object 20094_0006\"\n", "[1] \"Object 20094_0007\"\n", "[1] \"Object 20094_0008\"\n", "[1] \"Object 20094_0009\"\n", "[1] \"Object 20094_0012\"\n", "[1] \"SelectIntegrationFeatures\"\n", "[1] \"RunPCA on given features\"\n", "[1] \"FindIntegrationAnchors\"\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Scaling features for provided objects\n", "\n", "Computing within dataset neighborhoods\n", "\n", "Finding all pairwise anchors\n", "\n", "Projecting new data onto SVD\n", "\n", "Projecting new data onto SVD\n", "\n", "Finding neighborhoods\n", "\n", "Finding anchors\n", "\n", "\tFound 1210 anchors\n", "\n", "Projecting new data onto SVD\n", "\n", "Projecting new data onto SVD\n", "\n", "Finding neighborhoods\n", "\n", "Finding anchors\n", "\n", "\tFound 846 anchors\n", "\n", "Projecting new data onto SVD\n", "\n", "Projecting new data onto SVD\n", "\n", "Finding neighborhoods\n", "\n", "Finding anchors\n", "\n", "\tFound 932 anchors\n", "\n", "Projecting new data onto SVD\n", "\n", "Projecting new data onto SVD\n", "\n", "Finding neighborhoods\n", "\n", "Finding anchors\n", "\n", "\tFound 668 anchors\n", "\n", "Projecting new data onto SVD\n", "\n", "Projecting new data onto SVD\n", "\n", "Finding neighborhoods\n", "\n", "Finding anchors\n", "\n", "\tFound 718 anchors\n", "\n", "Projecting new data onto SVD\n", "\n", "Projecting new data onto SVD\n", "\n", "Finding neighborhoods\n", "\n", "Finding anchors\n", "\n", "\tFound 636 anchors\n", "\n", "Projecting new data onto SVD\n", "\n", "Projecting new data onto SVD\n", "\n", "Finding neighborhoods\n", "\n", "Finding anchors\n", "\n", "\tFound 761 anchors\n", "\n", "Projecting new data onto SVD\n", "\n", "Projecting new data onto SVD\n", "\n", "Finding neighborhoods\n", "\n", "Finding anchors\n", "\n", "\tFound 796 anchors\n", "\n", "Projecting new data onto SVD\n", "\n", "Projecting new data onto SVD\n", "\n", "Finding neighborhoods\n", "\n", "Finding anchors\n", "\n", "\tFound 626 anchors\n", "\n", "Projecting new data onto SVD\n", "\n", "Projecting new data onto SVD\n", "\n", "Finding neighborhoods\n", "\n", "Finding anchors\n", "\n", "\tFound 710 anchors\n", "\n", "Projecting new data onto SVD\n", "\n", "Projecting new data onto SVD\n", "\n", "Finding neighborhoods\n", "\n", "Finding anchors\n", "\n", "\tFound 294 anchors\n", "\n", "Projecting new data onto SVD\n", "\n", "Projecting new data onto SVD\n", "\n", "Finding neighborhoods\n", "\n", "Finding anchors\n", "\n", "\tFound 317 anchors\n", "\n", "Projecting new data onto SVD\n", "\n", "Projecting new data onto SVD\n", "\n", "Finding neighborhoods\n", "\n", "Finding anchors\n", "\n", "\tFound 290 anchors\n", "\n", "Projecting new data onto SVD\n", "\n", "Projecting new data onto SVD\n", "\n", "Finding neighborhoods\n", "\n", "Finding anchors\n", "\n", "\tFound 308 anchors\n", "\n", "Projecting new data onto SVD\n", "\n", "Projecting new data onto SVD\n", "\n", "Finding neighborhoods\n", "\n", "Finding anchors\n", "\n", "\tFound 336 anchors\n", "\n", "Projecting new data onto SVD\n", "\n", "Projecting new data onto SVD\n", "\n", "Finding neighborhoods\n", "\n", "Finding anchors\n", "\n", "\tFound 456 anchors\n", "\n", "Projecting new data onto SVD\n", "\n", "Projecting new data onto SVD\n", "\n", "Finding neighborhoods\n", "\n", "Finding anchors\n", "\n", "\tFound 477 anchors\n", "\n", "Projecting new data onto SVD\n", "\n", "Projecting new data onto SVD\n", "\n", "Finding neighborhoods\n", "\n", "Finding anchors\n", "\n", "\tFound 392 anchors\n", "\n", "Projecting new data onto SVD\n", "\n", "Projecting new data onto SVD\n", "\n", "Finding neighborhoods\n", "\n", "Finding anchors\n", "\n", "\tFound 422 anchors\n", "\n", "Projecting new data onto SVD\n", "\n", "Projecting new data onto SVD\n", "\n", "Finding neighborhoods\n", "\n", "Finding anchors\n", "\n", "\tFound 480 anchors\n", "\n", "Projecting new data onto SVD\n", "\n", "Projecting new data onto SVD\n", "\n", "Finding neighborhoods\n", "\n", "Finding anchors\n", "\n", "\tFound 259 anchors\n", "\n", "Projecting new data onto SVD\n", "\n", "Projecting new data onto SVD\n", "\n", "Finding neighborhoods\n", "\n", "Finding anchors\n", "\n", "\tFound 620 anchors\n", "\n", "Projecting new data onto SVD\n", "\n", "Projecting new data onto SVD\n", "\n", "Finding neighborhoods\n", "\n", "Finding anchors\n", "\n", "\tFound 711 anchors\n", "\n", "Projecting new data onto SVD\n", "\n", "Projecting new data onto SVD\n", "\n", "Finding neighborhoods\n", "\n", "Finding anchors\n", "\n", "\tFound 603 anchors\n", "\n", "Projecting new data onto SVD\n", "\n", "Projecting new data onto SVD\n", "\n", "Finding neighborhoods\n", "\n", "Finding anchors\n", "\n", "\tFound 568 anchors\n", "\n", "Projecting new data onto SVD\n", "\n", "Projecting new data onto SVD\n", "\n", "Finding neighborhoods\n", "\n", "Finding anchors\n", "\n", "\tFound 601 anchors\n", "\n", "Projecting new data onto SVD\n", "\n", "Projecting new data onto SVD\n", "\n", "Finding neighborhoods\n", "\n", "Finding anchors\n", "\n", "\tFound 279 anchors\n", "\n", "Projecting new data onto SVD\n", "\n", "Projecting new data onto SVD\n", "\n", "Finding neighborhoods\n", "\n", "Finding anchors\n", "\n", "\tFound 455 anchors\n", "\n", "Projecting new data onto SVD\n", "\n", "Projecting new data onto SVD\n", "\n", "Finding neighborhoods\n", "\n", "Finding anchors\n", "\n", "\tFound 654 anchors\n", "\n", "Projecting new data onto SVD\n", "\n", "Projecting new data onto SVD\n", "\n", "Finding neighborhoods\n", "\n", "Finding anchors\n", "\n", "\tFound 542 anchors\n", "\n", "Projecting new data onto SVD\n", "\n", "Projecting new data onto SVD\n", "\n", "Finding neighborhoods\n", "\n", "Finding anchors\n", "\n", "\tFound 493 anchors\n", "\n", "Projecting new data onto SVD\n", "\n", "Projecting new data onto SVD\n", "\n", "Finding neighborhoods\n", "\n", "Finding anchors\n", "\n", "\tFound 471 anchors\n", "\n", "Projecting new data onto SVD\n", "\n", "Projecting new data onto SVD\n", "\n", "Finding neighborhoods\n", "\n", "Finding anchors\n", "\n", "\tFound 526 anchors\n", "\n", "Projecting new data onto SVD\n", "\n", "Projecting new data onto SVD\n", "\n", "Finding neighborhoods\n", "\n", "Finding anchors\n", "\n", "\tFound 261 anchors\n", "\n", "Projecting new data onto SVD\n", "\n", "Projecting new data onto SVD\n", "\n", "Finding neighborhoods\n", "\n", "Finding anchors\n", "\n", "\tFound 358 anchors\n", "\n", "Projecting new data onto SVD\n", "\n", "Projecting new data onto SVD\n", "\n", "Finding neighborhoods\n", "\n", "Finding anchors\n", "\n", "\tFound 526 anchors\n", "\n", "Projecting new data onto SVD\n", "\n", "Projecting new data onto SVD\n", "\n", "Finding neighborhoods\n", "\n", "Finding anchors\n", "\n", "\tFound 829 anchors\n", "\n", "Projecting new data onto SVD\n", "\n", "Projecting new data onto SVD\n", "\n", "Finding neighborhoods\n", "\n", "Finding anchors\n", "\n", "\tFound 949 anchors\n", "\n", "Projecting new data onto SVD\n", "\n", "Projecting new data onto SVD\n", "\n", "Finding neighborhoods\n", "\n", "Finding anchors\n", "\n", "\tFound 705 anchors\n", "\n", "Projecting new data onto SVD\n", "\n", "Projecting new data onto SVD\n", "\n", "Finding neighborhoods\n", "\n", "Finding anchors\n", "\n", "\tFound 621 anchors\n", "\n", "Projecting new data onto SVD\n", "\n", "Projecting new data onto SVD\n", "\n", "Finding neighborhoods\n", "\n", "Finding anchors\n", "\n", "\tFound 701 anchors\n", "\n", "Projecting new data onto SVD\n", "\n", "Projecting new data onto SVD\n", "\n", "Finding neighborhoods\n", "\n", "Finding anchors\n", "\n", "\tFound 304 anchors\n", "\n", "Projecting new data onto SVD\n", "\n", "Projecting new data onto SVD\n", "\n", "Finding neighborhoods\n", "\n", "Finding anchors\n", "\n", "\tFound 409 anchors\n", "\n", "Projecting new data onto SVD\n", "\n", "Projecting new data onto SVD\n", "\n", "Finding neighborhoods\n", "\n", "Finding anchors\n", "\n", "\tFound 666 anchors\n", "\n", "Projecting new data onto SVD\n", "\n", "Projecting new data onto SVD\n", "\n", "Finding neighborhoods\n", "\n", "Finding anchors\n", "\n", "\tFound 541 anchors\n", "\n", "Merging dataset 6 into 5\n", "\n", "Extracting anchors for merged samples\n", "\n", "Finding integration vectors\n", "\n", "Finding integration vector weights\n", "\n", "Integrating data\n", "\n", "Warning message:\n", "\"Keys should be one or more alphanumeric characters followed by an underscore, setting key from integrated_gex_ to integratedgex_\"\n", "Warning message:\n", "\"Keys should be one or more alphanumeric characters followed by an underscore, setting key from integrated_gex_ to integratedgex_\"\n", "Merging dataset 7 into 5 6\n", "\n", "Extracting anchors for merged samples\n", "\n", "Finding integration vectors\n", "\n", "Finding integration vector weights\n", "\n", "Integrating data\n", "\n", "Warning message:\n", "\"Keys should be one or more alphanumeric characters followed by an underscore, setting key from integrated_gex_ to integratedgex_\"\n", "Warning message:\n", "\"Keys should be one or more alphanumeric characters followed by an underscore, setting key from integrated_gex_ to integratedgex_\"\n", "Merging dataset 9 into 1\n", "\n", "Extracting anchors for merged samples\n", "\n", "Finding integration vectors\n", "\n", "Finding integration vector weights\n", "\n", "Integrating data\n", "\n", "Warning message:\n", "\"Keys should be one or more alphanumeric characters followed by an underscore, setting key from integrated_gex_ to integratedgex_\"\n", "Warning message:\n", "\"Keys should be one or more alphanumeric characters followed by an underscore, setting key from integrated_gex_ to integratedgex_\"\n", "Merging dataset 4 into 2\n", "\n", "Extracting anchors for merged samples\n", "\n", "Finding integration vectors\n", "\n", "Finding integration vector weights\n", "\n", "Integrating data\n", "\n", "Warning message:\n", "\"Keys should be one or more alphanumeric characters followed by an underscore, setting key from integrated_gex_ to integratedgex_\"\n", "Warning message:\n", "\"Keys should be one or more alphanumeric characters followed by an underscore, setting key from integrated_gex_ to integratedgex_\"\n", "Merging dataset 5 6 7 into 2 4\n", "\n", "Extracting anchors for merged samples\n", "\n", "Finding integration vectors\n", "\n", "Finding integration vector weights\n", "\n", "Integrating data\n", "\n", "Warning message:\n", "\"Keys should be one or more alphanumeric characters followed by an underscore, setting key from integrated_gex_ to integratedgex_\"\n", "Warning message:\n", "\"Keys should be one or more alphanumeric characters followed by an underscore, setting key from integrated_gex_ to integratedgex_\"\n", "Merging dataset 8 into 10\n", "\n", "Extracting anchors for merged samples\n", "\n", "Finding integration vectors\n", "\n", "Finding integration vector weights\n", "\n", "Integrating data\n", "\n", "Warning message:\n", "\"Keys should be one or more alphanumeric characters followed by an underscore, setting key from integrated_gex_ to integratedgex_\"\n", "Warning message:\n", "\"Keys should be one or more alphanumeric characters followed by an underscore, setting key from integrated_gex_ to integratedgex_\"\n", "Merging dataset 1 9 into 2 4 5 6 7\n", "\n", "Extracting anchors for merged samples\n", "\n", "Finding integration vectors\n", "\n", "Finding integration vector weights\n", "\n", "Integrating data\n", "\n", "Warning message:\n", "\"Keys should be one or more alphanumeric characters followed by an underscore, setting key from integrated_gex_ to integratedgex_\"\n", "Warning message:\n", "\"Keys should be one or more alphanumeric characters followed by an underscore, setting key from integrated_gex_ to integratedgex_\"\n", "Merging dataset 3 into 2 4 5 6 7 1 9\n", "\n", "Extracting anchors for merged samples\n", "\n", "Finding integration vectors\n", "\n", "Finding integration vector weights\n", "\n", "Integrating data\n", "\n", "Warning message:\n", "\"Keys should be one or more alphanumeric characters followed by an underscore, setting key from integrated_gex_ to integratedgex_\"\n", "Warning message:\n", "\"Keys should be one or more alphanumeric characters followed by an underscore, setting key from integrated_gex_ to integratedgex_\"\n", "Merging dataset 10 8 into 2 4 5 6 7 1 9 3\n", "\n", "Extracting anchors for merged samples\n", "\n", "Finding integration vectors\n", "\n", "Finding integration vector weights\n", "\n", "Integrating data\n", "\n", "Warning message:\n", "\"Keys should be one or more alphanumeric characters followed by an underscore, setting key from integrated_gex_ to integratedgex_\"\n", "Warning message:\n", "\"Keys should be one or more alphanumeric characters followed by an underscore, setting key from integrated_gex_ to integratedgex_\"\n", "Warning message:\n", "\"Keys should be one or more alphanumeric characters followed by an underscore, setting key from integrated_gex_ to integratedgex_\"\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[1] \"IntegrateData\"\n", "[1] \"GEX integration done\"\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Centering and scaling data matrix\n", "\n", "PC_ 1 \n", "Positive: FCN1, LYZ, AIF1, IFI30, MNDA, SERPINA1, CST3, CD14, SPI1, S100A9 \n", "\t S100A8, CD68, GRN, AC020656.1, LST1, CSTA, VCAN, MS4A6A, CFP, CLEC7A \n", "\t CSF3R, CYBB, CEBPD, TNFAIP2, TYMP, NCF2, MPEG1, CTSS, SLC11A1, LRP1 \n", "Negative: CCL5, NKG7, SYNE2, CTSW, GZMA, CST7, KLRD1, TRBC2, GZMH, CD247 \n", "\t FGFBP2, GZMM, GZMB, PRF1, IL32, GNLY, CD7, ISG20, HOPX, KLRK1 \n", "\t ADGRG1, RORA, CD3E, TRBC1, IFITM1, C12orf75, CD2, SPON2, LCK, LIME1 \n", "PC_ 2 \n", "Positive: CD79A, MS4A1, BANK1, TNFRSF13C, RALGPS2, HLA-DQA1, LINC00926, IGHM, BLK, IGHD \n", "\t PAX5, MEF2C, HLA-DRA, LTB, AFF3, HLA-DQB1, CD74, TCF4, HLA-DRB1, ADAM28 \n", "\t TCL1A, IGKC, NIBAN3, FCER2, VPREB3, SWAP70, FCRL2, HLA-DOB, FCRLA, CD79B \n", "Negative: NKG7, CST7, CTSW, GZMB, GZMA, GNLY, SRGN, PRF1, S100A4, FGFBP2 \n", "\t KLRD1, GZMM, CCL5, ID2, GZMH, PFN1, IFITM2, CD247, ANXA1, TMSB4X \n", "\t CD7, FCGR3A, HOPX, CCL4, ADGRG1, KLRB1, CD63, IFITM1, SYNE2, SPON2 \n", "PC_ 3 \n", "Positive: TYMS, RPS11, PTMA, RPL23, TMSB10, STMN1, NPM1, MIF, PCLAF, H3F3A \n", "\t TK1, ZWINT, PPIA, SUB1, CLSPN, HIST1H4C, ANP32B, TMSB4X, TOP2A, ESCO2 \n", "\t RRM2, CBX5, NUSAP1, TUBB, HSP90AB1, NASP, RPLP0, CDT1, H2AFZ, ACTG1 \n", "Negative: NKAPL, FADS1, FOXO3, LINC02076, NEAT1, CBARP, DTHD1, NUMB, A2M-AS1, ZFAT \n", "\t DENND3, BTBD8, S100A9, TRAV4, LRRCC1, PTK7, INPPL1, ZEB2, AL592295.5, XIST \n", "\t S100A8, TOGARAM2, LINC02384, SLC7A5, MAPK6, SLC2A3, BRIP1, ITGAX, SAT1, THBS1 \n", "PC_ 4 \n", "Positive: KLRF1, IGFBP7, FCGR3A, CHST2, PLAC8, CLIC3, FCER1G, TYROBP, SH2D1B, PRF1 \n", "\t SPON2, GNLY, GZMB, ZBTB16, KLRB1, CD160, FGFBP2, RHOC, S1PR5, IL2RB \n", "\t TMIGD2, TRDC, HOPX, PTGDR, CD247, LAT2, TXK, CD38, CXXC5, KLRD1 \n", "Negative: IL7R, TRAC, CD3D, CD3G, PCLAF, CD3E, IL32, TYMS, MKI67, TK1 \n", "\t TOP2A, NUSAP1, CENPU, MAL, CD6, BCL11B, PCNA, BIRC5, CDK1, NCAPH \n", "\t ZWINT, CD5, GINS2, SMC2, BUB1B, TCF7, CACNA1I, CLSPN, MAD2L1, NELL2 \n", "PC_ 5 \n", "Positive: CD3D, CD3E, CD3G, RPS11, RPL23, TRAC, IL32, TMSB10, TRGC2, CD8B \n", "\t LIME1, NPM1, CD8A, S100A4, SPOCK2, TMSB4X, RPLP0, IL7R, CD5, SSR4 \n", "\t HSP90AB1, LDHB, CD2, TRBC2, MIF, TRBC1, LINC01871, JUN, DUSP2, HERPUD1 \n", "Negative: NKAPL, FOXM1, HIST1H1B, RRM2, ASF1B, MKI67, PCLAF, TYMS, FADS1, XIST \n", "\t PCNA, CDK1, TK1, ASPM, NUSAP1, SPON2, LRRCC1, CLSPN, KIFC1, CD38 \n", "\t CDT1, PKMYT1, BIRC5, GTSE1, GINS2, ZWINT, LINC02076, CKAP2L, TOP2A, BRIP1 \n", "\n", "Warning message:\n", "\"The default method for RunUMAP has changed from calling Python UMAP via reticulate to the R-native UWOT using the cosine metric\n", "To use Python UMAP via reticulate, set umap.method to 'umap-learn' and metric to 'correlation'\n", "This message will be shown once per session\"\n", "21:01:49 UMAP embedding parameters a = 0.9922 b = 1.112\n", "\n", "21:01:49 Read 14672 rows and found 30 numeric columns\n", "\n", "21:01:49 Using Annoy for neighbor search, n_neighbors = 30\n", "\n", "21:01:49 Building Annoy index with metric = cosine, n_trees = 50\n", "\n", "0% 10 20 30 40 50 60 70 80 90 100%\n", "\n", "[----|----|----|----|----|----|----|----|----|----|\n", "\n", "*\n", "*\n", "*\n", "*\n", "*\n", "*\n", "*\n", "*\n", "*\n", "*\n", "*\n", "*\n", "*\n", "*\n", "*\n", "*\n", "*\n", "*\n", "*\n", "*\n", "*\n", "*\n", "*\n", "*\n", "*\n", "*\n", "*\n", "*\n", "*\n", "*\n", "*\n", "*\n", "*\n", "*\n", "*\n", "*\n", "*\n", "*\n", "*\n", "*\n", "*\n", "*\n", "*\n", "*\n", "*\n", "*\n", "*\n", "*\n", "*\n", "*\n", "|\n", "\n", "21:01:51 Writing NN index file to temp file C:\\Users\\mjopp\\AppData\\Local\\Temp\\RtmpU1Va3H\\file19f105a122e1\n", "\n", "21:01:51 Searching Annoy index using 1 thread, search_k = 3000\n", "\n", "21:01:55 Annoy recall = 100%\n", "\n", "21:01:56 Commencing smooth kNN distance calibration using 1 thread\n", " with target n_neighbors = 30\n", "\n", "21:01:58 Initializing from normalized Laplacian + noise (using irlba)\n", "\n", "21:01:59 Commencing optimization for 200 epochs, with 646060 positive edges\n", "\n", "21:02:13 Optimization finished\n", "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[1] \"libintegration/ig_dimplot 8 6\"\n", "[1] \"Saving to file libintegration/ig_dimplot.png\"\n", "[1] \"Saving to file libintegration/ig_dimplot.pdf\"\n", "[1] \"Saving to file libintegration/ig_dimplot.svg\"\n", "[1] \"Saving to file libintegration/ig_dimplot.data\"\n" ] } ], "source": [ "\n", "integratedList_sample = performIntegration(finalList$data, \"libintegration\", features.integration = finalList$features, gex.method.normalization=\"LogNormalize\", gex.method.integration=\"rpca\", add.do=FALSE, run.parallel=FALSE)\n" ] }, { "cell_type": "code", "execution_count": 11, "id": "e12501d5", "metadata": {}, "outputs": [ { "data": { "image/png": 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"text/plain": [ "plot without title" ] }, "metadata": { "image/png": { "height": 420, "width": 420 } }, "output_type": "display_data" } ], "source": [ "\n", "ElbowPlot(integratedList_sample$integrated, ndims=30, reduction = \"igpca\")\n" ] }, { "cell_type": "code", "execution_count": 12, "id": "26b77730", "metadata": { "lines_to_next_cell": 2 }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[1] \"Scale Data\"\n", "[1] \"dim.reduction igpca\"\n", "[1] \"RunUMAP Data\"\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "21:02:18 UMAP embedding parameters a = 0.9922 b = 1.112\n", "\n", "21:02:18 Read 14672 rows and found 10 numeric columns\n", "\n", "21:02:18 Using Annoy for neighbor search, n_neighbors = 30\n", "\n", "21:02:18 Building Annoy index with metric = cosine, n_trees = 50\n", "\n", "0% 10 20 30 40 50 60 70 80 90 100%\n", "\n", "[----|----|----|----|----|----|----|----|----|----|\n", "\n", "*\n", "*\n", "*\n", "*\n", "*\n", "*\n", "*\n", "*\n", "*\n", "*\n", "*\n", "*\n", "*\n", "*\n", "*\n", "*\n", "*\n", "*\n", "*\n", "*\n", "*\n", "*\n", "*\n", "*\n", "*\n", "*\n", "*\n", "*\n", "*\n", "*\n", "*\n", "*\n", "*\n", "*\n", "*\n", "*\n", "*\n", "*\n", "*\n", "*\n", "*\n", "*\n", "*\n", "*\n", "*\n", "*\n", "*\n", "*\n", "*\n", "*\n", "|\n", "\n", "21:02:20 Writing NN index file to temp file C:\\Users\\mjopp\\AppData\\Local\\Temp\\RtmpU1Va3H\\file19f104d021379\n", "\n", "21:02:20 Searching Annoy index using 1 thread, search_k = 3000\n", "\n", "21:02:24 Annoy recall = 100%\n", "\n", "21:02:25 Commencing smooth kNN distance calibration using 1 thread\n", " with target n_neighbors = 30\n", "\n", "21:02:27 Initializing from normalized Laplacian + noise (using irlba)\n", "\n", "21:02:28 Commencing optimization for 200 epochs, with 607300 positive edges\n", "\n", "21:02:41 Optimization finished\n", "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[1] \"FindNeighbors Data\"\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Computing nearest neighbor graph\n", "\n", "Computing SNN\n", "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[1] \"FindClusters Data\"\n", "Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck\n", "\n", "Number of nodes: 14672\n", "Number of edges: 496246\n", "\n", "Running Louvain algorithm...\n", "Maximum modularity in 10 random starts: 0.9177\n", "Number of communities: 14\n", "Elapsed time: 2 seconds\n", "[1] \"libintegration/dimplot_umap 12 8\"\n", "[1] \"Saving to file libintegration/dimplot_umap.png\"\n", "[1] \"Saving to file libintegration/dimplot_umap.pdf\"\n", "[1] \"Saving to file libintegration/dimplot_umap.svg\"\n", "[1] \"Saving to file libintegration/dimplot_umap.data\"\n", "[1] \"libintegration/dimplot_umap_project 24 40\"\n", "[1] \"Saving to file libintegration/dimplot_umap_project.png\"\n", "[1] \"Saving to file libintegration/dimplot_umap_project.pdf\"\n", "[1] \"Saving to file libintegration/dimplot_umap_project.svg\"\n", "[1] \"Saving to file libintegration/dimplot_umap_project.data\"\n" ] } ], "source": [ "\n", "obj.integrated = preprocessIntegrated(integratedList_sample$integrated, \"integrated_gex\", \"libintegration\", resolution=0.5, num.pcs=10, dim.reduction=\"igpca\", with.hto=FALSE)\n", "\n" ] }, { "cell_type": "code", "execution_count": 13, "id": "23d9481a", "metadata": { "lines_to_next_cell": 2 }, "outputs": [], "source": [ "obj.integrated@reductions$ig.umap = NULL" ] }, { "cell_type": "code", "execution_count": 14, "id": "9fb276a7", "metadata": {}, "outputs": [ { "data": { "image/png": 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U7QTdkiX7CSf0u79qKzeubNERk53Z0jOAxzLwy5DA3q+QMZ9FpIgk5z21bZ2SEm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\n/KvR5GH84zRJ0OtxHNmJ+Xz64cETdDLS8KP4rzRecjfWQ9g3YjI0bMVzLOj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V0j6Js8zdmbEJP1/QQ6bjOAzBEfRuEZOhoVK80XNnYGWI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\nANCRrTsAAAA6snUHAABA5/8DPAgXSNK66KsAAAAASUVORK5CYII=", "text/plain": [ "plot without title" ] }, "metadata": { "image/png": { "height": 720, "width": 720 } }, "output_type": "display_data" } ], "source": [ "options(repr.plot.width = 12, repr.plot.height = 12)\n", "\n", "DimPlot(obj.integrated, label=T)" ] }, { "cell_type": "code", "execution_count": 15, "id": "c172ba81", "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\t\n", "\t\n", "\n", "\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\n", "
A data.frame: 10 × 5
LibrarySampleNamepatientsMpointCondition
<int><chr><chr><int><chr>
1 120094_0001_A_B1-3 1SYMPTOMATIC
4 220094_0002_A_B1-3 2SYMPTOMATIC
7 320094_0003_A_B1-3 3SYMPTOMATIC
2 420094_0004_A_B4-6 1ASYMPTOMATIC
5 520094_0005_A_B4-6 2ASYMPTOMATIC
8 620094_0006_A_B4-6 3ASYMPTOMATIC
3 720094_0007_A_B7-9 1SYMPTOMATIC
6 820094_0008_A_B7-9 2SYMPTOMATIC
9 920094_0009_A_B7-9 3SYMPTOMATIC
101220094_0012_A_B12-141CONTROL
\n" ], "text/latex": [ "A data.frame: 10 × 5\n", "\\begin{tabular}{r|lllll}\n", " & Library & SampleName & patients & Mpoint & Condition\\\\\n", " & & & & & \\\\\n", "\\hline\n", "\t1 & 1 & 20094\\_0001\\_A\\_B & 1-3 & 1 & SYMPTOMATIC \\\\\n", "\t4 & 2 & 20094\\_0002\\_A\\_B & 1-3 & 2 & SYMPTOMATIC \\\\\n", "\t7 & 3 & 20094\\_0003\\_A\\_B & 1-3 & 3 & SYMPTOMATIC \\\\\n", "\t2 & 4 & 20094\\_0004\\_A\\_B & 4-6 & 1 & ASYMPTOMATIC\\\\\n", "\t5 & 5 & 20094\\_0005\\_A\\_B & 4-6 & 2 & ASYMPTOMATIC\\\\\n", "\t8 & 6 & 20094\\_0006\\_A\\_B & 4-6 & 3 & ASYMPTOMATIC\\\\\n", "\t3 & 7 & 20094\\_0007\\_A\\_B & 7-9 & 1 & SYMPTOMATIC \\\\\n", "\t6 & 8 & 20094\\_0008\\_A\\_B & 7-9 & 2 & SYMPTOMATIC \\\\\n", "\t9 & 9 & 20094\\_0009\\_A\\_B & 7-9 & 3 & SYMPTOMATIC \\\\\n", "\t10 & 12 & 20094\\_0012\\_A\\_B & 12-14 & 1 & CONTROL \\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "A data.frame: 10 × 5\n", "\n", "| | Library <int> | SampleName <chr> | patients <chr> | Mpoint <int> | Condition <chr> |\n", "|---|---|---|---|---|---|\n", "| 1 | 1 | 20094_0001_A_B | 1-3 | 1 | SYMPTOMATIC |\n", "| 4 | 2 | 20094_0002_A_B | 1-3 | 2 | SYMPTOMATIC |\n", "| 7 | 3 | 20094_0003_A_B | 1-3 | 3 | SYMPTOMATIC |\n", "| 2 | 4 | 20094_0004_A_B | 4-6 | 1 | ASYMPTOMATIC |\n", "| 5 | 5 | 20094_0005_A_B | 4-6 | 2 | ASYMPTOMATIC |\n", "| 8 | 6 | 20094_0006_A_B | 4-6 | 3 | ASYMPTOMATIC |\n", "| 3 | 7 | 20094_0007_A_B | 7-9 | 1 | SYMPTOMATIC |\n", "| 6 | 8 | 20094_0008_A_B | 7-9 | 2 | SYMPTOMATIC |\n", "| 9 | 9 | 20094_0009_A_B | 7-9 | 3 | SYMPTOMATIC |\n", "| 10 | 12 | 20094_0012_A_B | 12-14 | 1 | CONTROL |\n", "\n" ], "text/plain": [ " Library SampleName patients Mpoint Condition \n", "1 1 20094_0001_A_B 1-3 1 SYMPTOMATIC \n", "4 2 20094_0002_A_B 1-3 2 SYMPTOMATIC \n", "7 3 20094_0003_A_B 1-3 3 SYMPTOMATIC \n", "2 4 20094_0004_A_B 4-6 1 ASYMPTOMATIC\n", "5 5 20094_0005_A_B 4-6 2 ASYMPTOMATIC\n", "8 6 20094_0006_A_B 4-6 3 ASYMPTOMATIC\n", "3 7 20094_0007_A_B 7-9 1 SYMPTOMATIC \n", "6 8 20094_0008_A_B 7-9 2 SYMPTOMATIC \n", "9 9 20094_0009_A_B 7-9 3 SYMPTOMATIC \n", "10 12 20094_0012_A_B 12-14 1 CONTROL " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "\n", "df = read.table(\"samples2condition.df\", header = TRUE)\n", "df[order(df$SampleName),]\n" ] }, { "cell_type": "code", "execution_count": 16, "id": "bcdaebb2", "metadata": { "lines_to_next_cell": 0 }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[1] \"SYMPTOMATIC TRUE\"\n", "[1] \"SYMPTOMATIC TRUE\"\n", "[1] \"SYMPTOMATIC TRUE\"\n", "[1] \"ASYMPTOMATIC TRUE\"\n", "[1] \"ASYMPTOMATIC TRUE\"\n", "[1] \"ASYMPTOMATIC TRUE\"\n", "[1] \"SYMPTOMATIC TRUE\"\n", "[1] \"SYMPTOMATIC TRUE\"\n", "[1] \"SYMPTOMATIC TRUE\"\n", "[1] \"CONTROL TRUE\"\n", "[1] SYMPTOMATIC ASYMPTOMATIC CONTROL \n", "Levels: CONTROL ASYMPTOMATIC SYMPTOMATIC\n", "[1] \"TP 1 TRUE\"\n", "[1] \"TP 2 TRUE\"\n", "[1] \"TP 3 TRUE\"\n", "[1] \"TP 1 TRUE\"\n", "[1] \"TP 2 TRUE\"\n", "[1] \"TP 3 TRUE\"\n", "[1] \"TP 1 TRUE\"\n", "[1] \"TP 2 TRUE\"\n", "[1] \"TP 3 TRUE\"\n", "[1] \"Ctrl TRUE\"\n", "[1] TP 1 TP 2 TP 3 Ctrl\n", "Levels: Ctrl TP 1 TP 2 TP 3\n" ] } ], "source": [ "\n", "annotateList.sample_condition = list(\n", " list(name=\"SYMPTOMATIC\", selector=\"^20094_0001\"),\n", " list(name=\"SYMPTOMATIC\", selector=\"^20094_0002\"),\n", " list(name=\"SYMPTOMATIC\", selector=\"^20094_0003\"),\n", " list(name=\"ASYMPTOMATIC\", selector=\"^20094_0004\"),\n", " list(name=\"ASYMPTOMATIC\", selector=\"^20094_0005\"),\n", " list(name=\"ASYMPTOMATIC\", selector=\"^20094_0006\"),\n", " list(name=\"SYMPTOMATIC\", selector=\"^20094_0007\"),\n", " list(name=\"SYMPTOMATIC\", selector=\"^20094_0008\"),\n", " list(name=\"SYMPTOMATIC\", selector=\"^20094_0009\"),\n", " list(name=\"CONTROL\", selector=\"^20094_0012\")\n", "\n", ")\n", "\n", "obj.integrated = annotateByCellnamePattern( obj.integrated, \"condition\", annotateList.sample_condition, order=c(\"CONTROL\", \"ASYMPTOMATIC\", \"SYMPTOMATIC\"))\n", "\n", "annotateList.sample_condition = list(\n", " list(name=\"TP 1\", selector=\"^20094_0001\"),\n", " list(name=\"TP 2\", selector=\"^20094_0002\"),\n", " list(name=\"TP 3\", selector=\"^20094_0003\"),\n", " list(name=\"TP 1\", selector=\"^20094_0004\"),\n", " list(name=\"TP 2\", selector=\"^20094_0005\"),\n", " list(name=\"TP 3\", selector=\"^20094_0006\"),\n", " list(name=\"TP 1\", selector=\"^20094_0007\"),\n", " list(name=\"TP 2\", selector=\"^20094_0008\"),\n", " list(name=\"TP 3\", selector=\"^20094_0009\"),\n", " list(name=\"Ctrl\", selector=\"^20094_0012\")\n", ")\n", "\n", "obj.integrated = annotateByCellnamePattern( obj.integrated, \"tp\", annotateList.sample_condition, order=c(\"Ctrl\", \"TP 1\", \"TP 2\", \"TP 3\"))\n", "\n" ] }, { "cell_type": "code", "execution_count": 17, "id": "d9ffb061-a50e-450f-a5fa-2e04e49529de", "metadata": {}, "outputs": [], "source": [ "options(repr.plot.width = 12, repr.plot.height = 10)" ] }, { "cell_type": "code", "execution_count": 18, "id": "5a2d5aeb", "metadata": { "fig.height": 20, "fig.width": 24, "lines_to_next_cell": 2 }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[1] CONTROL ASYMPTOMATIC SYMPTOMATIC \n", "Levels: CONTROL ASYMPTOMATIC SYMPTOMATIC\n", "[1] Ctrl TP 1 TP 2 TP 3\n", "Levels: Ctrl TP 1 TP 2 TP 3\n", "[1] \"CONTROL_Ctrl\"\n", "[1] 3449\n", "[1] \"CONTROL_TP 1\"\n", "[1] 0\n", "[1] \"CONTROL_TP 2\"\n", "[1] 0\n", "[1] \"CONTROL_TP 3\"\n", "[1] 0\n", "[1] \"ASYMPTOMATIC_Ctrl\"\n", "[1] 0\n", "[1] \"ASYMPTOMATIC_TP 1\"\n", "[1] 884\n", "[1] \"ASYMPTOMATIC_TP 2\"\n", "[1] 1202\n", "[1] \"ASYMPTOMATIC_TP 3\"\n", "[1] 198\n", "[1] \"SYMPTOMATIC_Ctrl\"\n", "[1] 0\n", "[1] \"SYMPTOMATIC_TP 1\"\n", "[1] 3024\n", "[1] \"SYMPTOMATIC_TP 2\"\n", "[1] 3180\n", "[1] \"SYMPTOMATIC_TP 3\"\n", "[1] 2735\n", "[1] \"Finishing Plot\"\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Warning message:\n", "\"\u001b[1m\u001b[22mRemoved 131 rows containing missing values (`geom_point()`).\"\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[1] \"Combining Plots\"\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Warning message:\n", "\"\u001b[1m\u001b[22mRemoved 430 rows containing missing values (`geom_point()`).\"\n", "Warning message:\n", "\"\u001b[1m\u001b[22mRemoved 35 rows containing missing values (`geom_point()`).\"\n", "Warning message:\n", "\"\u001b[1m\u001b[22mRemoved 47 rows containing missing values (`geom_point()`).\"\n", "Warning message:\n", "\"\u001b[1m\u001b[22mRemoved 9 rows containing missing values (`geom_point()`).\"\n", "Warning message:\n", "\"\u001b[1m\u001b[22mRemoved 101 rows containing missing values (`geom_point()`).\"\n", "Warning message:\n", "\"\u001b[1m\u001b[22mRemoved 161 rows containing missing values (`geom_point()`).\"\n", "Warning message:\n", "\"\u001b[1m\u001b[22mRemoved 131 rows containing missing values (`geom_point()`).\"\n", "Warning message:\n", "\"Graphs cannot be vertically aligned unless the axis parameter is set. Placing graphs unaligned.\"\n", "Warning message:\n", "\"Graphs cannot be horizontally aligned unless the axis parameter is set. Placing graphs unaligned.\"\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[1] \"Combining Legend\"\n", "[1] \"split_dimplot_downsample_no 24 20\"\n", "[1] \"Saving to file split_dimplot_downsample_no.png\"\n", "[1] \"Saving to file split_dimplot_downsample_no.pdf\"\n", "[1] \"Saving to file split_dimplot_downsample_no.svg\"\n", "[1] \"list case\"\n", "[1] \"Saving to file split_dimplot_downsample_no.1.data\"\n", "[1] \"Saving to file split_dimplot_downsample_no.2.data\"\n", "[1] \"multi list case\"\n", "[1] \"Saving to file split_dimplot_downsample_no.2.1.data data.frame\"\n", "[1] \"Saving to file split_dimplot_downsample_no.2.2.data waiver\"\n", "[1] \"Saving to file split_dimplot_downsample_no.2.3.data waiver\"\n", "[1] \"Saving to file split_dimplot_downsample_no.2.4.data waiver\"\n", "[1] \"Saving to file split_dimplot_downsample_no.2.5.data waiver\"\n", "[1] \"Saving to file split_dimplot_downsample_no.2.6.data data.frame\"\n", "[1] \"Saving to file split_dimplot_downsample_no.2.7.data data.frame\"\n", "[1] \"Saving to file split_dimplot_downsample_no.2.8.data data.frame\"\n", "[1] \"Saving to file split_dimplot_downsample_no.2.9.data waiver\"\n", "[1] \"Saving to file split_dimplot_downsample_no.2.10.data data.frame\"\n", "[1] \"Saving to file split_dimplot_downsample_no.2.11.data data.frame\"\n", "[1] \"Saving to file split_dimplot_downsample_no.2.12.data data.frame\"\n" ] }, { "data": { "image/png": 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gQNAABECSzoxyRopgCAhyG6TBa8YZhNCdQAAABcgKAT1AAAAFyAoBPUAAAAXICg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We5S3xNb2gIugTSNGj2o/Sdu1OEvAcdsTpRSXgTH9PP\nnaHLJHZ7Zt0Pecues9wJJm/MEHQJ3PEt/r3zrbT+xo+ysBN0054hjvWYd7Lrfd2s5ccp8WoT\nmehDHOKvHLXrZkHzLVIGDUGXwPJv8e/ftYaW/QyxkE/rfy3ZHff813Jfzr0oBrDi/Xxfh+Pe\nBs2kn7VLCvksaH6kkjBqCLoEIgpaE0XXiZY9j9fCDX2POO4MWk12YOjXskc47mvPK4L+UfJm\n/WlDCo0Zgi6Bxd/i/R1o5WJY/UKKB/DzHTmvGkuyzrMTV2uWmnHDPe35Xj9rPQ6mniYk0ZYh\n6BKIPMQhRzd+1H5Hc52KaNKFymNpziv2g6NXerfxogdtsKI9izOEYnSDiRG7VwrNGYIugZg9\n6H6OaL9EnLlq/5+9Jp+bFIp7BL0q6B/pDTHM390miUDfLiB39aDvf7suZOUhb93SzwmThqBL\n4A5xrGvP5pLmH96Mk3c5QrIw579/7z/0btGuPhbW6Geb318wcaK3Z6VFd/3oV3F+ECcJwWoW\nfYtNU17TnuVAnbH0tfyu3WJxrB7kUO7k050efJU7QlbshMa7doT3v92P2Za78WcGQQMvLPkW\n28PuVe2ZqQPPEqW7gR40Z6U3jLu6djcL7O5H1Z0oLDPohR2OJubNZpWhB89f+/4G5kGDlQy+\nxaen8Y3/ejjyljc0V1AEDXE0+Aha60V3Nxnl2XJ/lHmt98IORyvo1WPQ2nP+AD1osJiKoy/T\nnj21jBbw14M45HkrVdLKLLsC/OyW80QBf1cHrQw/9xPBmBzgeJScJ7sbPvaDQ0PzP2p7TpU0\nBJ0Z59kG/fQ0aWjemNc16R9jZLSjJEPP5ixSnsqZrRxNUm4ZyLqpue1ydYT07sJpMN+e53aE\n6w1t+lmg+jlV0BB0Zpyr/XCh8i0+9YyUINryugYtu3Smn1kZhnbMmU3mvHZ+AeunGPQzc/Ud\nYe5Bz+V8S5pNxtywugdtW/hKoscBQWfGR/U+XNh/i0+zgl59PMjUGbrd00Fzzlwbszn3xymT\nfl6zG2xRBjlYP4dDCzrvqJ1yZtODdqsFrSLifKURMgSdGR/Vx3Dhgh608LMPcXSXG3fXt2nH\ng9k7ei7nuaB9KUPtQcvdIJ8nk/jg2w8zOSsHKqN7wu7//PAqz8HSaM4QdGbsq+NbVR/0hVZB\nM3vnrm3Nm/Wd6G7SF2sunVCEUUovei7nuWMV34Jm7ey6vhst+3V5xzyXs+LnmR6HL5RRfs3P\niXKGoDNjz8+p7Lqnw1MsvTU2dnPc/n+z2fzdrK5K14OWx92moVe/QUrcch43h08/yws1m50i\n0xMuPGdlJzh+oOLX0A26mVMOckDQmVFVX4xdD/qB4XAWx+bm543NwXx8w4efpRxeLf3n7C9y\nc8p5sme3ehippZ/I2Pagh7vAwnPu/WwLWozXefbz8GjQd/nOQNBZcq226lO1QTPeed40kh52\nO7pJdhs/Tbo/HBwMcvSrc2Y8574HPfpiz/06yyCSGIfOnon23A/YjRjae9ANZtKvqX7KDYLO\nE33iqHFIOD4+Ki67snau70Y/4lbd4fNdkjCWszI42j4zX+djBofOq3UYKXH/zhujOTO1OQ9f\nF2aEwyLoVEFD0HkyIw45ate0abVdd4eEXv3c3aK479E9iDj6E7GDIdIAylB60Mb83Nxn2jVM\n5Cz6zl3S2st4zoH8bF60CUGDeerqevv3ok/v1wWtHA2aPY+2++y/UsoNMF91ifh/r0g45Kzk\navbvAvi5xdane4CcZbjDQ8JA1eJNmKk3TcIQB3DgUB3akypHdaHR45AH3oMjQ//DG5KRo+9c\n1TGTc5+rTFrZLsRBN2cYb84hs/n2rA9vaDvFoPWSjTfxoQoEnRnXup2HpE8c1U8SCl0o51e6\nla03wvSgrROhM5bHbM7dIJKyL+xXBvPz2GnCfC8onMuZGX6WMTePQqXcoJ/4Tnancwg6N66H\nutoaV1/ZThKy/i4Gcog0kDde+wE6vTVnLOiZnPVLvYfHKYEqNdKBzjlpt5z5Y6MHHS7njj7j\nZDtCCLoEBmPQrD8iVDp64Q68mRyg06bb5X78bWKIQ5yDjTYyOjq7IGdDW7BeqGK5IUdgPysX\nrDD1cCX0u6pA0CUwOCTcbDZy+lcvkafgDVqORP92gv4tVBzyZKztWpWAo6ODTnOTNfstK2Yz\n5zFDB+1BdzP59c5z17DDvesACLoE9DHoDb9IxZhjMHHjNT+Ic97Cz21X+qaO33LUofXsjNtx\n9EP9pq+9Iq8AknvB34bm+W+4d43M4Ib9ysid2o5D+vnVGH9mbTtuskYPGixEE0czS+NJjnUw\nxvrG7Q3LLy1xDYvz3r/ts19OKX62nozt1KyPlQbltTsj+9t2nH9bQzP+byHYBf3E5E6Rzf3M\nynrEnpBP3+BHKb+iSYd9axUIugS0Q8J2lkZjjM1Gn2jg7/2+v22G7h9xd7SNWTTsIrDdlMr+\n1Avfth2hcnOqX95/7vp15fh5TNDqOIfvrEcRRymitxHZ0BB0CZhjo91AdN+z8/x+Nj+3yJtz\ntF06lqBBB2VwCbIyqhHAz9/WHaHsQfMDlfZYRWRcYM7KNZtK2w7fg5bwkTqTSG8OQafma1vV\nb5fuSXXn12FeGivmcfgf22CKN4by6EabeW9O73N4rcId+M5ZE7IYRFpVQ5MxPwte5SAHY3R2\nhd5zZvoJWe3C71j8docoCRo0BJ2WA7//bXcdlQ9By0400/7x1OP47mD2cQ7uZ37SKk2PYwT/\nOSv321aOv1dVUqXP2c4vE+cHzcNvb1W4B985Pz19SiWLAxblWlkPfA8eWGhPdXdRs6gpQ9BJ\nOba3wz1uuxbtRdCbjWjOAnFgeHc1Bd+9n7/HW7Stv5HYGyFyVnaEyjSDdfWUfKuCtgRtjbjE\nnD8/P+VpQb5Ee7Ia3pi/R86rsPZAkDE2jLk9YeipEhNA0EnZdzcqf+Mt2oc4PtnG6Mx5O+ut\nSENI2orVHCylPQLkzJg2iuR1DPpb9/NQHSLRV83TfcSpgvaf89PnINgn7c8avoWfx1vz8AhF\nW7K+DjNA0EmRLZjfLcZPj2PQdj136/jj8T6HPPYetutUivafc8tnf95qeMvR+2lDVtId9fPv\nq7UfzUrK+RbrLebP/rlcvjptpbsxvpGts9G1ZPSgi6dvwW2L9tODZkxt0Iv53/gqY1h0ogfN\nxBy7+H0OGwFyZnxnKC2xWBjTOU9Kg/0y297vVSxM1oMOkvNnS/dEOTPrVtb/pnJum/BU0CNJ\nC0GjB104/G64Lfvq7K1n9/R0v6H/N9WiG23046Kjg3a/4806jaED5dyHvFnqjX8TOYsjlWlH\nD/d9/eMonTsLYXL+7JLmfY9le8L/zbTn8QPBlkG+ajNGD7p43pQfy9zWF1/iWNGDnm7Q2hQO\ne8uWdh4z9L01W0OonD9baXwyce8T5470v6mcvw3sW01EXF7Otx50c7qwfbLoSGWuvyEGn0fP\neNvPEcrdYejxfgg6Kef2rDfnUtU+xqDNR0tH6ub8zOdviD6eZaOpBp1KHf5zbuHH3s3/Nv1Q\nh1NJ//vfEkOP7ghVVQyE7VQRv4TLuZvOsXyYf8rP0sszU87ZSIuOcOE3BJ2WZt6oeHys1jfo\ndrDuUz7yfOce0Xnm3eexPnSDpRlHatF2fOfM6cZGP5WYXdP+35Q6BoJ2MLQlcMeq+CREzp88\n50/zhtB+EE16eiul7Q6T9lwjDQg6Mee3Wj6+vHnpQXNJf97Zg55AmlkOkFqb9e9kDzrRHDDf\nOXPk+SshDj8C0dQ8Zo+xfNWo4+M/508ZcntS1quhRR/VQesdAAAgAElEQVTDqf+c5KwKBF0C\n5lnv9l910RNbMa2jR5rZ8ezViEF8VCUJujhYJ432+Fv+toqP91HsPHEFhRlqVHEERZuVxIc3\nZK/Dq5/nu86c6QYdEgiaHHccF5o9DnP905Nl4XLcJtmZEzgsrXt9VTywMmc594t37rxeSqiN\n8Lv42bIXLCXnfqD/k/npZijYz6IMmM4aPejHYm2DtrVinz3o/rF9ozk7E/HGenHwPvQn/9tc\n8ca8DSgpQY/nPD2Hg0zQfnaEXnoYJk795+mcQzdoCJocqwXd479NT13fJtAUEb3L4cz6HaGK\n78FRxdD2LexK1jP3WKH7WZezknAA+oynWvOso0NUrQOCJoc/QQdo1W59jpRdDmc89OyYag/P\n0wu+546/x6L9VZTit0r3sSpn0XvupnEEYnIs2j6+H6s9Q9Dk8CBo3pTDHBUyl5G7DPzsrWfX\n9+/8xj19BmtGGizhDTkMVubMZ9l1//ZXFXpjfprdTGOGoB+M9YLWhDG8zcxa5s99/46NbRDy\n8+oe9MDRvneIUzlbnUwzaR9j0OJghXmPeWoiY8v8EPT3r9OZxvuAoMmxWhy9oZnajfZ5j/Op\n/obDRDsSeJpdwEL5ecbQw73gIPeA3liAh5OxyhjHXA96cRufmQY9sueLljMETY7Vh97tH/H8\nn/8e9CTDBj1o3zGq4YCfnt1Az//81K5lak9oz1bPnIShfZxTcR6D9n2l4fQJwggD/RA0OeYa\n9KGu6sNVWzS43ajk3785YWw2G/e6OTDwc66Cns6513Lv5yb6+cC9MBquuoKEn9e254YFRyhP\nXlvzxD4wUmuGoMkx06B37Y++bfWXjG792fSghTL+Wbp3m41vQ5s34jBbuOc3u5tVOX/qghbP\n//ntQY+i9N/Gd4YxKuLA2vYsr9d0GkHy3Jpn7/zl881sQNDkmG7Qp6o+s3NdnbSXjG//yTt1\njTb+/bN07wL7OUGTdmVVzoPRjfZ/ar7dI9mL9ZuzsDHTpz3rO0aP77eCle1ZTN4w9Dwapvf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3u6HV3pxYKld6r5l/UuS8sAet5KxMrLNn\nT5b4OS+/95dx8lU/GR54RwhBJ0U5nVLvv5xecj3U1da4+ipaD9q1a/esq0MsE407TO0mKD9n\n8awfiEbOIyh+XpIzf2A07efhCIhfIOik6Ke8a8err4bFeK3UOM5du4E6lKsoUnSgy82ZickE\n/HHffUbOo0hDu+esTUbSD1TCAkFT4XraV/Wdr43V41giDmW2qN6iEx950825Z5mgzcsHn1Md\np2jQzVmOcSxqzkwOdUhLxwgZgibE3nbjGBeijdktNIfafVZmJfmv4zL+3965qCdua2FUnek4\ngeQQmmRoaKv3f80T32+SbG/JsmzWOt9JJkCF+SUW27Js0sy55ffiD8KfPWn8/Gk+PBubVHNu\nK+hF/1lv74QK+gH5nHF5RiPR5uyWDGmDNba6gM+ARHNukRh64I8UDhEmn/MielpuZzpWftYk\no3hY0vqael9+Wth6u/aQ86LaLtGU95DzErqfg+UNEfYH04ziUUlyQC9dlNQhVXMkmbMHaaZ8\n1Jw7azrWDzrVKB6TFAf08mWjXRBHFBI19OFy1u2cnY6zkDHdKB6QNOfsfPysddQjKjNJM2dP\nuiknMQOdaM6ew1l3z+6OkHLKQ+7hOJm+n20OiffiVqtybRw059RiTjJnvx3Ckpgxpz3kHon7\nxynRdaMhSMcbh845nZiTzdnfz1FJfsgdm32cebV/yDkO+8h5T45myG2K4NoFxmYCbpKBPQ1o\nM/vIef/sIucQsxzRYMgdAQZ0HHi3xGHlnJddbXRbGHJHYL1ezNWMn2t4t8RhzfH8on+87MjQ\nDLkjsFov5sXzCxV0zZqrC9Zren+sOZ5f9jWeEfQRWLuC3tGAXpXVcv6xL2uszYqC1jszNII+\nAsxBx2GtnH/8+N7tJuWGtXIuR/LLnnZXEPQRYBVHHFYTx4/c0Ss1vkPWq6B7v/YAgj4C9GIc\nVsr5u7DLa2hK6JqVL12wp6R5ax8BejEOq1XQ+dKvXU2NrsvaF//aUc68tY8AvRiHtSroYmnu\nrryxLilf/CsyvLWPAL0Yh3VyLiY4mOHowIkqDby1j8B6lR10WSXn0s/70sbKrDiVlP/c0/FY\nBH0EVujFl30dS4nDGu+WfOo5FwbLOFrWOxir9zYJjaCPQPhe3Nly/kis8kFYB42fG9asoF/2\nVXog6COwUgVd/A7f9H5Zp4Le4erclVlnKqme4dB7ihpBH4EVT/XeT60RgUA5P7X/7OxuE3VD\nqPHcCVp3dgn3lDOC3gvXuqsumcou99594cTx9NQZ1rs7LzYE6+f89PRUR9xeu+elJ+sHIMZ4\nfnrqBNruqezpoxBB74Rb/Q3Jp+Jy6M+9O8OJo6T8uzyaYhjO+Q37GeLLiJBz39BlwPVlA48a\n64j1c85TNgc6zDnl0BH0Prhl1YD+VNkt/+uze69vLz7l/3vSfT/Xe4Wj4by/azbOZ92cdbl7\n0om44EU/3N7K2uO5HssvT/2JjoJBzkmPZQS9C67qVA3oi/r4/vne/75kz158GlLeXPm5V2G8\ndPF71hRZN2ddu7n+f+kKw8HYzoGsA6a8wXiuR3Hv8rmd2f9kU0bQu0BddDWgzyr/Js6bOvfu\n9mz+SVv83Dt01VPzMQ29cs66dXMZc5NnN+beUrB9rdqdS4zx3O4VtosZm/n+KuUX3Q7lNIcz\ngt4FN10P6P6visAVR3VzU0E3w3mI39MmyLo5F5QJF/IoD2PVhtYvzYx0E/1RZ6ZXzbk7kJ+a\nPRXdqzbalBMfzwh6L5gHdPUVyj4NP40q6GYPvNGE0c6JDmlPVsu5jLQXc30c6+VlOHfUj9nr\naRNlvfHcD7i5uTOVMci4Pk7r86xrgaD3wloVR1lodJXRGdlmNSdedPixWmVXRdo7GJv/qCUx\nDFvrjjnIeTb9j8DOrX0xd361sxw+z7sOCHovrDegRwV0vny02jMcl83V7uJh56FX/CAspzaa\nuKuFukNdDArpVigeT54gUQTdFNBPwwGt2/3D3u2ppYygU6a7v1f9zlYSh2nHsOtlQ+V8nPnR\nCDn3rdFMQdefebZ5pO4S6f0nHWM868Fwbm4rMX0O1rZOMWUEnTKGAV0e9f4KdtS71sUQbfZz\nuyfe+S1/9kRYP+f+FFJ1rkqzD26TcyPonX1Pk40I43l0yLu6Mf/R2elrZNxK+aUzSZ0OCHov\nVAP6rVg3+qEuvfs82h3Wz50aulNRjP2sf78cqYhuWCnnxsyjnZXuSg2Dn5sTN19+yZ89QVYc\nz+1eSq3obsFRPKov6OY/Tu9zEEHvhdXOvHqySLp/wtXIHL8HBchRWCvnWhXdsOt1du1eiMnQ\n9b9+HcrQK+U8/Pjr7RJ2DgM2eyfdEdz7Iw0Q9F6odw2fi93EU/8+j3YNdV3JYKSO1dGRh8fz\np8aKOQ8/Bsub7Tl3FhccLuZ1czYxfOBoTyXJixgg6L1QD+h7cfWvwX0+DT8NZ0jbCrqHydDp\nDWhv1sp57Of875eXp4mce4ewPJ4/NdYbz/P83A96cD6Wz/OHBUEfgVBnEramNjxqWNl1/u33\n9PthBXHYrrY2+PxL8wjWWoTNWY8vT1XQGcGjMwt9NiAoCPoIhDo11lFuaD0+lSLB8bwuAXOu\n5zyMtOvq+g4h5xkMd1LKmSTTIzsZtz8SG88I+ggEGNDm9f1D+lJG0Avp52tNOacpnftRe27A\nTggzB91Z0WFjWDinlzKCPgLe14gYLv6yPrZ73kSiQ3pFvN8ttTjqM1VctCd7P1rMoSrodt7f\n+uDU/YygD0GQym663igYj+fEhvSK+L9beqWd+6HkLOZpUEi7H510zAj6CASp7PQ8b5jE4fv0\neyHAu2V6GqlhvKfi//T7IFDO88bzYEQnNtePoI9AmAGtq9mOKboj+aG8EbaCnn7wS382yfvJ\nd0Oo625r53RdTdIVB4I+AoEG9NyHji669iiE+CCsfs0K+0WTs5y5I3oo56SSRtBHIEgvzvbz\ncJ/wcYj+bqmV8UjfJ6uD5Tx3RKdaPecg6COwhTiO+nV5LuK/Wx4r35r447m6wnla1XMOgj4C\niCMOvFviQM4NRHEE6MU4kHMcyLmBKI4AvRgHco4DOTcQxRGgF+NAznEg5waiOAL0YhzIOQ7k\n3EAUR4BejAM5x4GcG4jiCNCLcSDnOJBzA1EcAXoxDuQcB3JuIIojQC/GgZzjQM4NRHEE6MU4\nkHMcyLmBKI4AvRgHco4DOTcQxRGgF+NAznEg5waiOAL0YhzIOQ7k3EAUR4BejAM5x4GcG4ji\nCNCLcSDnOJBzA1EcAXoxDuQcB3JuIIojQC/GgZzjQM4NRLEXrlVXqZLeffRiOMg5DuQ8C6LY\nCbdqDN8Y0KtCznEg53kQxT64Zc2APo/vpRdDQc5xIOeZEMUuuKpTNaCv6m18N70YCHKOAznP\nhSh2gbroZkBfDXdH3pzDQs5xIOe5EMUuuOl6QJ/Vx6vKLv276cVAkHMcyHkuRLEXmgFdcGpu\nHR9iAR/IOQ7kPAui2AvVqFXqXev7pb9jSC+Gg5zjQM6zIIq90Csr7uq5d1/kbTky5BwHcp4F\nUaRMd3+vv983+CveJh0Sco4DOS+GKFKGAR0Hco4DOS+GKPZCNYQzdf/++dVf3k8vhoOc40DO\nsyCKvVAN6Iu6FAdVPnr3bbJFx4Sc40DOsyCKvVAN6HtW7Cb2F47Si+Eg5ziQ8yyIYi/Us3T3\nS6aeB2df0YvhIOc4kPMsiOII0ItxIOc4kHMDURwB9ZiQcxzIOQ7GKGJnD3FY0LHrPDSBLYjB\n9q9y+y2IwfavcpstSK0fIBCPOqBjs/2r3H4LYrD9q0TQEJBHHdCx2f5Vbr8FMdj+VSJoCMij\nDujYbP8qt9+CGGz/KhE0BORRB3Rstn+V229BDLZ/lQgaAAA6IGgAgERB0AAAiYKgAQASBUED\nACQKggYASBQEDQCQKAj6iFzrbr1kKrvcJx7tuFRLj1mNLWtyyZbWD53d9vqQcxweOOck8oew\n3OruPhVd/zz56Fmjb1Zjy5pcsqX1Q2e3vT7kHIdHzjmF/CEst6zq7k+V3fK/Pt0P738fnI15\njS1qcsmWNg+d2/b6kHMcHjpnBH04rurUfN9b/k1v7+pt4vHu+yvmNbaoyQVb2j50ZtvrQ85x\neOycEfThUJf664TO6ktPf/xf1dV5f8W8xhY1uWBL24fObHt9yDkOj50zgj4cN133ff+XjbP6\neFXZxfmY2Y0tanLBlrYPndn2+pBzHB47ZwR9RBYO6ILTojbdzGxy1Kqz8WZAz217fcg5Dg+c\nM4I+IosGtFLv+XcrT+3FLRnQM5tctqXNY+a2vT7kHIcHzhlBH5FFA7rkPrXgaEljM5sctTpn\nQM9ve33IOQ4PnDOCPgrd1ZTV78w5TAbLL6dGqrsxyzPMfcicxvt3LtmOoJBzHMi5vH3epkHy\nGAZ0eSz5y3IseeGAdjdmeYa5D5nTeLriIOcVIOfy9rkbBzui6u23YjXmh3Iffs5Ufkbq5Eid\n19iiJpdtaVOczG17fcg5Dg+cM4I+IlXfzztZ6pIPo3u5tN7BkjOvZja5bEurh85ve33IOQ4P\nnDOCPiL1/tLznNVB96x41GQlMauxZU0u2tLqofPbXh9yjsMD54ygj0g9TO7FNbWmHp0/6nl6\nBdG8xhY1uWhLuw+d1fb6kHMcHjhnBA0AkCgIGgAgURA0AECiIGgAgERB0AAAiYKgAQASBUED\nACQKggYASBQEDQCQKAgaRoyuCZadr1/VDV/Xc9beqTr/LE9utZ0S9cZIG0HOcdhzznQnjBgN\n6M61Ai7tKP74/udH87hmSBub/NjsspUJQ85x2HPOdCeMMAzo57rMyJ6be0/q0gzf+sbPzPjd\nPR8KcYw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"text/plain": [ "plot without title" ] }, "metadata": { "image/png": { "height": 600, "width": 720 } }, "output_type": "display_data" } ], "source": [ "p=makeUMAPPlot(obj.integrated, dim1 = \"condition\", dim2=\"tp\", group.by=\"idents\", downsample = FALSE)\n", "save_plot(p, \"split_dimplot_downsample_no\", 24, 20)\n" ] }, { "cell_type": "code", "execution_count": 19, "id": "55b8abb0", "metadata": { "fig.height": 20, "fig.width": 24, "lines_to_next_cell": 2 }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[1] CONTROL ASYMPTOMATIC SYMPTOMATIC \n", "Levels: CONTROL ASYMPTOMATIC SYMPTOMATIC\n", "[1] Ctrl TP 1 TP 2 TP 3\n", "Levels: Ctrl TP 1 TP 2 TP 3\n", "[1] \"Downsampling 198\"\n", "[1] \"CONTROL_Ctrl\"\n", "[1] 198\n", "[1] \"CONTROL_TP 1\"\n", "[1] 0\n", "[1] \"CONTROL_TP 2\"\n", "[1] 0\n", "[1] \"CONTROL_TP 3\"\n", "[1] 0\n", "[1] \"ASYMPTOMATIC_Ctrl\"\n", "[1] 0\n", "[1] \"ASYMPTOMATIC_TP 1\"\n", "[1] 198\n", "[1] \"ASYMPTOMATIC_TP 2\"\n", "[1] 198\n", "[1] \"ASYMPTOMATIC_TP 3\"\n", "[1] 198\n", "[1] \"SYMPTOMATIC_Ctrl\"\n", "[1] 0\n", "[1] \"SYMPTOMATIC_TP 1\"\n", "[1] 198\n", "[1] \"SYMPTOMATIC_TP 2\"\n", "[1] 198\n", "[1] \"SYMPTOMATIC_TP 3\"\n", "[1] 198\n", "[1] \"Finishing Plot\"\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Warning message:\n", "\"\u001b[1m\u001b[22mRemoved 7 rows containing missing values (`geom_point()`).\"\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[1] \"Combining Plots\"\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Warning message:\n", "\"\u001b[1m\u001b[22mRemoved 30 rows containing missing values (`geom_point()`).\"\n", "Warning message:\n", "\"\u001b[1m\u001b[22mRemoved 10 rows containing missing values (`geom_point()`).\"\n", "Warning message:\n", "\"\u001b[1m\u001b[22mRemoved 7 rows containing missing values (`geom_point()`).\"\n", "Warning message:\n", "\"\u001b[1m\u001b[22mRemoved 9 rows containing missing values (`geom_point()`).\"\n", "Warning message:\n", "\"\u001b[1m\u001b[22mRemoved 8 rows containing missing values (`geom_point()`).\"\n", "Warning message:\n", "\"\u001b[1m\u001b[22mRemoved 8 rows containing missing values (`geom_point()`).\"\n", "Warning message:\n", "\"\u001b[1m\u001b[22mRemoved 7 rows containing missing values (`geom_point()`).\"\n", "Warning message:\n", "\"Graphs cannot be vertically aligned unless the axis parameter is set. Placing graphs unaligned.\"\n", "Warning message:\n", "\"Graphs cannot be horizontally aligned unless the axis parameter is set. Placing graphs unaligned.\"\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[1] \"Combining Legend\"\n", "[1] \"split_dimplot_downsample 24 20\"\n", "[1] \"Saving to file split_dimplot_downsample.png\"\n", "[1] \"Saving to file split_dimplot_downsample.pdf\"\n", "[1] \"Saving to file split_dimplot_downsample.svg\"\n", "[1] \"list case\"\n", "[1] \"Saving to file split_dimplot_downsample.1.data\"\n", "[1] \"Saving to file split_dimplot_downsample.2.data\"\n", "[1] \"multi list case\"\n", "[1] \"Saving to file split_dimplot_downsample.2.1.data data.frame\"\n", "[1] \"Saving to file split_dimplot_downsample.2.2.data waiver\"\n", "[1] \"Saving to file split_dimplot_downsample.2.3.data waiver\"\n", "[1] \"Saving to file split_dimplot_downsample.2.4.data waiver\"\n", "[1] \"Saving to file split_dimplot_downsample.2.5.data waiver\"\n", "[1] \"Saving to file split_dimplot_downsample.2.6.data data.frame\"\n", "[1] \"Saving to file split_dimplot_downsample.2.7.data data.frame\"\n", "[1] \"Saving to file split_dimplot_downsample.2.8.data data.frame\"\n", "[1] \"Saving to file split_dimplot_downsample.2.9.data waiver\"\n", "[1] \"Saving to file split_dimplot_downsample.2.10.data data.frame\"\n", "[1] \"Saving to file split_dimplot_downsample.2.11.data data.frame\"\n", "[1] \"Saving to file split_dimplot_downsample.2.12.data data.frame\"\n" ] }, { "data": { "image/png": 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7vtr4mBrGEByNkNknN+tTqXpPxE5+yM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\ncnbG0KnBWPxsmDOChkA4qmP/xfZedN2W4/Tz45zBCmY5I2gIhJ0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asswo1kpcDbqgczSIoBdGkJ9jy7k2c/kEQa+NyBp0hT5s57s2abQ5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\nRgNoIqJGA2giokYDaCKiRgNoIqJG+xenUm7mDyHf1gAAAABJRU5ErkJggg==", "text/plain": [ "plot without title" ] }, "metadata": { "image/png": { "height": 600, "width": 720 } }, "output_type": "display_data" } ], "source": [ "p=makeUMAPPlot(obj.integrated, dim1 = \"condition\", dim2=\"tp\", group.by=\"idents\", downsample = TRUE)\n", "save_plot(p, \"split_dimplot_downsample\", 24, 20)\n", "\n" ] }, { "cell_type": "code", "execution_count": 20, "id": "e2509db2", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Warning message in dir.create(\"libintegration/de\"):\n", "\"'libintegration\\de' already exists\"\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[1] \"Processing cluster 0 with a total of 3412 cells\"\n", "[1] FALSE\n", "[1] \"Processing cluster 1 with a total of 2517 cells\"\n", "[1] FALSE\n", "[1] \"Processing cluster 2 with a total of 1399 cells\"\n", "[1] FALSE\n", "[1] \"Processing cluster 3 with a total of 1223 cells\"\n", "[1] FALSE\n", "[1] \"Processing cluster 4 with a total of 941 cells\"\n", "[1] FALSE\n", "[1] \"Processing cluster 5 with a total of 929 cells\"\n", "[1] FALSE\n", "[1] \"Processing cluster 6 with a total of 910 cells\"\n", "[1] FALSE\n", "[1] \"Processing cluster 7 with a total of 889 cells\"\n", "[1] FALSE\n", "[1] \"Processing cluster 8 with a total of 836 cells\"\n", "[1] FALSE\n", "[1] \"Processing cluster 9 with a total of 656 cells\"\n", "[1] FALSE\n", "[1] \"Processing cluster 10 with a total of 357 cells\"\n", "[1] FALSE\n", "[1] \"Processing cluster 11 with a total of 349 cells\"\n", "[1] FALSE\n", "[1] \"Processing cluster 12 with a total of 191 cells\"\n", "[1] FALSE\n", "[1] \"Processing cluster 13 with a total of 63 cells\"\n", "[1] FALSE\n", "NULL\n", "[1] \"0\"\n", "[1] \"1\"\n", "[1] \"2\"\n", "[1] \"3\"\n", "[1] \"4\"\n", "[1] \"5\"\n", "[1] \"6\"\n", "[1] \"7\"\n", "[1] \"8\"\n", "[1] \"9\"\n", "[1] \"10\"\n", "[1] \"11\"\n", "[1] \"12\"\n", "[1] \"13\"\n" ] } ], "source": [ "\n", "dir.create(\"libintegration/de\")\n", "\n", "exprdfTT = makeDEResults(obj.integrated, assay=\"RNA\", test=\"t\")\n", "write.table(exprdfTT, \"libintegration/de/expr_test_t.tsv\", sep=\"\\t\", row.names=F, quote = F)\n", "writexl::write_xlsx(exprdfTT, \"libintegration/de/expr_test_t.xlsx\")\n", "\n" ] }, { "cell_type": "code", "execution_count": 21, "id": "e9483966", "metadata": { "lines_to_next_cell": 2 }, "outputs": [ { "data": { "image/png": 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TABh51Z/loN3ptuX117JjzmkuSvbOLa3bhG9u6Ghe4GgNqBgAMAAKBOe239aSn0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dE6nCMUjCOM9vy4Zom4pavYbNyvrTg3YA5sWZ\nfR+bx5cCWH9eYWRXvgDeObUYwMpTyue+zj2fw7KM/OpqqeJz1BjrgA+C3Mr21fEyzIsLCxyM\nApQnOjish93DBQfmmiC75jde1ELsmlEMYNlxZ17ikkxDcxl2oNaHYhPk3SCHoZERSlcj7LE4\nHrbqIIrjSLb2iTaad6N56gvNDfHnI/+s2LvBMAzjdB4mP/vhG/jrb62WT8LCzL7TlqJ3Q41W\nXvUA6urcXLrTc3JtHpQ6VsXM2tV+Qf/KBf0rDzz0Xj6kAsCbgwsB5QETNw8AODDPpLYBI/no\nDi8qBHxmizrmv7316M2h/YSb4vjPulq3U+vypm7paMS7QQidJhpOCiPeDULQqiasNSpaObIs\n1JCHGMbVsMDBGCIpMhfwFL5G5dK1ESSBkeKTpWL+hSI7pxQDWJnkD1uuMGeqxFANk9WsEKGb\nrE4YigOTrTzrdd+3kbkWmwEgJEJZvhFngghDra47GDWbj+6G0pXdWQBGLeUNBx2ap7ayLNF/\nz6yiPbOKlhxtpg4mhmGYloDi2da5aZfa2KRu0J6Wt08dZC0hAl9efArgvbG9jbzg4Ok9xDdv\nJaR1DDKvP005Vj21lh2Uvn/+olB8P12cb5lYAmDdOdW9E7KrrDzV7dtbj4Q7aTSmo2UFFLop\niGy/usTOMQGIiFf4BChATcOEK3amnI3KBTApkpdSTEuEBQ5GAcoTVePg/EIA8+LMJ07F6FD5\nvIYdONe7Qdjt3XA6zcG7QSw8HHgtNuNabLmaMtJokHeDBA6BNwabAAC2ZXqryStEzPRiGJh/\nEaxMio82T32hGRL2RhWAxN96Se7fPaMIoglnhmEYxikonp5oYUYCh91sDy0BsPq0zgJmcJgL\nt08EtL0bAgceelONHaGx1BGGow1C3o2bh8vppti7QYgHt1Mft9Z4Kdqx+88H7bysn7V+VBnw\n/7N33gFR3Pn7fwBRsYCiolGDesnpz7tczLVc2kWjJmp07QXsDUTEhtjBBnaCXRG7RkCxr1GT\nqNFc2uXuvORy5RtTFBSFpS5FVBR+f3x2h2Hazu7OFuD9+guW2Z1Z2sw8n+f9PIg92RpmgcPl\nsBDW3mMCAbBaNJYdLoC8G4QLIYGDUIKzSKi3wDE+2J0JoH91f8SCg74nNmad2PiAC9/ePTcf\nwLRN/uwD1r7ChYOygAyByYJ5Nxg232Fy72vd+CIAiw7boqTc/t7UovL+yhwAY5dLf4uOrswB\nMEbqqwqRXbYhedrm+tvYWkrahux//bUJ0Dz2pMRwLLvzLy/3sF9/sc3mA+u9Gx8fyQAvLF2A\nnUNJ1jL5tcd/+lMJAK3K8Di4XBhtX9YRkHeDIAjC+YjjxgQcWJILeAgCKVzF9ohC8MwaUft8\nBY0t68YXA1h0uOpqRODdUGPjNb9mewCZ36sVCDivhH5nZrc3wI/GFzMgTOKrIVIZ/Gp4/MhT\nLsTKYoDa3Z98AKwZU7zkaNMxy2rA1QJBOAgSOAir4bwbjObtH3Efq5mHBHB6832rSq2cgPg2\nOLzHQwA7rzWUe8pSeb8iLMVNuRsO9W5sjygAELHdxqgzizX1kijLK81aPOE+VpCZlK1MLuH0\n5vs+fk8Ezls3R+zdYHDejcWDHrw1LBfAO+OlVSqCIAjCHbDo3dAEdjEJKFkeBCgP3u6IKHih\nV2GXN3Djov+mUCM3O7M1vNDbuxLA9C3NASTHGQCo6XyRbM/dFl749KkHZEpe+EMuO2cVAAjf\nWnVdxGSaXsHCZ0muRSlTUOg1v/+DjR+o9b0mzimA2Y2iBubdYEh6NwjC5ZDAQShh0SKxKcQI\nNJsrWrDtL5Nt4VW/ErzkCm6Vm32wNbyQv7GP7xNY4siynP980xjAunNWjDBw78s274YAOe8G\nQ9K7wajvUyH3JTUor12w/PC+k54V9LeNWNB6BO9TQcNZTWwtkfNuwNR0U19cRWzz0opF9n9R\nH/C/tP/Opf2lkvoIa33jksPUM25lKxZXRhAEQRBiFLwbDAXvxsxeDwFsuyK7qMPgp2ZysLwJ\na0+sL/ZmgRdVE80CvYDv3ZCkV3CH6CGlnxwvjTstqwKw12Sz1Qowy0YT8z0+55VQ9m5wbJ5m\nBOAlHV0qjV/L8qOrDGOWWRZTJCfBJYna53tx793s06p+EAphHzZzetN9AEPmutcSJlEHIYGD\nsMCxddkAWFvqqU33AQw1/+fatzDP1x9F+VW/RXLeDW6I46dvG0OqQpKp5rN2VvtHz79159/M\nO6L1SjzCoODdUIA7YajxbigrIzazbFgpgNcGAEBCiBGuHhmw2buhFcfWZgOQqwTScERIAfFy\nDUN50enDQ3dy0xsA8G/3GMCQOc6YanY07EKQv8C19mwjgLwbBEEQrsdikqUTUBmuwUd8GmWh\nnmyFY8b25kBzAGw1hmoAACAASURBVN2HVG1wOTn9V2+g9+gOML/rkoJ6XV4zXj9565uPmwOY\nnVjt5p8/cdy4ucQKnHL8HD9oP3xr86OrDGre1/F12R6eAHAiPmt4VLXrXsmJpH5T2/ebqvSC\nLITl5r8btTRfgYZtbp4ca0iONYyOCVg1qgRAuw6PAEyxpJcRhNtCAgdhHZk/+GwLL+T/Exfb\nN/iYAyNlZW02G2Lbr+KJjVk+Ta3zbmgFy0f89RtFP/69KYAImUYxh6Lsr2FTDP/9wqj8Ipx3\nw9o2VrcibWMWgOI8b06tMJ/4W+6YWVBi9GrZ9jGA7TMK4ZQflsJsixrvRkqcAUCzZx6XFVX7\nu1BI86rpWGuRJQiCIDSEeTd2ziqo5620GfNuxAwtBRB7yuSb4LwbXOaXmj2+ObjTyYSskwlZ\n9qxXMe9G6hoDgKAlAQqpZ8owQYfJHD/9zWorq+RMigJxo4sBH4VmFj6VFR6e9Wyp7eNLPGJy\nc7xXnbB6/kUZ8m4QbgIJHIQFmHeDMXTuM9t4UyRT1qsVdzmrAvNu8FMP6jWoADA6OiA5zpAc\nZ5AbfeTfzGvr3ZCDO1/yH7To6LNo9tM8WFTMKt7EZuQevzNb753ZWuryhhTHsTsy39/SKZXZ\nN364aYsrR0zi3Hyo6DbmT/OKvRsMtugkl4R669+NAHT69QMWKcIEjpqOtReCBEEQdQQ1qZka\nEj+5CCJT7ZNyjybNnhxellNSWE/uzOUgtHr77Fw5Ic60GHB8XTaAkYuELk7m3eAT+p4/4P/T\n3/KaNnsqvk7j6ya3/231ahDzbgDYHZnfqg1ysrxhin2VGB368OAdAH0mPssd9hcXf/ri4k+v\n9asqauG8G4IFKuWmG8mGYG44aNkxVn1CBShEzYYEDsI6mHfjo8MZUJcIyBoxJEtV+KSuVmXV\nOxyTC2B8rOlMwLWxWAU71RVke9vTc2HOR/TDJJtfQ3ssfp8VqKHeDUb+/frTEvz3LczbtzCv\nfsOKcStbcSf+Gdtkr8+U628czbnt9zw8KyEa8T0Rn+XdEA0aVTx55Mke8fKuPLvt3qCZbWul\nd4NB3g2CIAiXwxSNw8ssVMly3g0BAu/GhT13AbwbIjtcWZSr9jZk0cAHEKWtcT0s3FoU0yCY\nwHH95C0A3Yd1kntN9jbFLYECaYM5dhns2u/8rkzA9B2ICy4B0KFzmaDjzOJ7V2nf4PgirRWA\nm5/lqp/fkfNuQF704RB03MixZmwxAPt79whCW0jgILSBX4ttEe7kcTI+C6hX/sgT5oXug0ty\nAUy0u8NMvQQjh8C7wVCTxqTs8pD0bmybUQhgpmNGJzjvhgt7Rh26a65X2CJbPtbGwWHRu8FQ\nSGLfOaugfWcAeFBU79i67Cb+FQDOJ2YCALzqeVc+LfeARyW7ljq7rVSDgyYIgiDcG6d5Nxhy\ndaTie345mJ8x8FcPUP1mntWgBHS08HSBc0H9279+6hYAFqshhi0GMIHjo8MZzdrCS3HEwwbn\nyHPdSgdMl15MYpbMetLtYUD1i5ZJa1pGDymNHiLMSe0z0Yr1DMECVeAvywAAJDoQ0nh4aPlq\nlbaMTzkcEjgIq4kNKgH8Y1IlDGypa7KDlkjowcqeguy79QF8fCQDAFDt3zQ7TxgN3uzemPNu\nKJO2MctPSpU2B33LKtZuhcCuogbbvBu1gN1z8wFM2yQI4LAwB+RQ7wY/CjdltQFA8NJqesfd\nmz7hW5uzEF8+w6PanNlSvS1F01ORM3Gy3ZogCIJwHxT8C9Yimbbm518+TqTFsLAtn8ZPAd8p\nG1qw5a5eomkUKOo4scElAGJSmmyYWAR4cNMl7JT98398AQyYDgDRKU22hBXmZ3sD+CTtNgCg\nPuTfO0umY+5mPqcS7g+NVJq2lVOjAKyfUASAn23fuNmTM1vvKcwmK3g3GBa9GwzybtREmtaB\nu/868BYJTZn06uNf8E4Tm0KMAObu8es9JjB1TbVbNUH/qCTDotoA2DK9KtdDpXfDYmmWMbv+\niPltAOxbmAdr4kI41D/xV/6VAP6bb7oNtejy2DipCNULWW3wbnx44A6APpOs0PjFBorIvmUA\nEi75WLt3a9Hcu8Eq32bI9LPc/lEbp4ZFlg4pBbC6+sLLrtkFANo+r9QB3Kr9Y/bBqEWt9y/K\nKy2sN3ldC5YtCmBw9SDSQRG1Nj+FIAiCcBB2Ssyf6X8G8IbuFwrbmP2MQlejDTUoHGe23gcw\neJbS3X73oZ2OmOdoVgWVAFgmteoGnpNXvysTgE7KdsF9i5gZOeO7xkAD2w4eUnFaAuo3eqrf\nlckdSdzpxqcStOyAH7Ms4MzWezY8cUuYEcDsRCflZHF3EM7ZHcHRxJo+4xpK7RQ4UlNTk5OT\n9Xq9TqcbPXp0v379/Pzo70czfk6vf+BLCe+dpHfDIldS0l94k51dqm7yd0fmw+ShaijoKmNn\nPuVF7REy2RyCkvYPku4C6B9qUtmXDi4FsPqMxpnSNmOVdwMycdnRQ0phjhlX4Pj6bAAjF9YM\nb4sYe+JUYKlHVoEX/sCGR6q+vbsj8z29UPG02u9n8NIA/c57+p33dOHVrnuOr89W+J5bVPHc\nH5svrD8+kuHhVQmpEDiCIIg6hZ06hcKNvTP54sJPAF579zmLW6pE7N1g2FCUJg7j+POwXABA\nE867wRglZXzg2mTfGtHR4r6GzWvDfiJ8mHdjz/x8ACEbq13PqAkL47wb68YXA/j92wWNm+Pt\ncVUz2ptCjQDmJpluhZJjDeAFiyqwY2YBFLPMiJpIA29tp0rc0WZcCwWOmJiYuLg49rFer9fr\n9WFhYbt27XLtUdUOkmMNb79b9T/xRHzWs13A9XKnbcgGMGKB6b+/nHdj8zQjeE0KD4urhERJ\n555AhmCov+tTY8Fgw6KAycig3HN2Ij4LAL+NnPNuWIRlbZSUKJawqYPzbthsUYFTvBsOQs67\nwYg71XjX7IJdsx9P36K02aUDd2Du0+U4vzsTwAD7hn2mb2l+ZNkThQ1GLGjNRCWYY1mupKQH\nPGe6hNXvvAfAmNOgXZcyew6DIAiCqB0krzIAGL3MdAGmRvUwnVBEt9MqUfZuSGIxWVMNyt4N\nDm4uo1NndqJscnSVAcCYZdL37WokHi5I7tqJYjXHwGari3O9AQwVNaReSLoL4O9Xmr3wSjG3\nQeoaA+AdtCTAYiufoxH/CjnNuwHgUEyufxtMsHIlj9CEBrXw7l9IbXuLN2/ejIuL0+l027dv\nDwwMzMjIiIiISExMnDt3bufOnV19dHWCjw5nqI/2PLoyp5nUiYyfwPRB0gPwUhXkznzKMwsM\nwX9zgWhij3fDHpXBKpYPLwWQnVMv8brQPykZl23Ru8GoWd4NFsY5IEyz9SgbvBsMFqvBv6ji\nfnUFi0t874Z5QaYmfc+dRtrGLAAj5tseD0wQBFGbYBctTOCwluRYA+CtZq1ewJawQvC8CZKs\nGFECYEWaUqXoJ8dvA3hrZEcNvRsCJK0canyp3LWEOIyjx/CO4u0t1rJwMItEMxUneX6yvsC7\nwRB7N9iPprzcM2qfybixb0EegCkbWiw6zBIxhLkYnHeDUW7uaLOIsndjzZhiAEuOUgxHDaMe\njajUOG7cuAEgNjY2MDAQQGBg4JIlS/R6/ffff08Ch/0IzpF8FwOAEQtaszAnZTjvBqPwfoPG\nzU1r3eLUJZhlCCZwCJDrD7fIxX13czMawJwNIRgWlfNuALiSkt68XZVEIta/46cUAeDOOgIc\n0ZPiBFXF/WEeHP7PUdm7wegrlWBip3eDcSohC8DQSCumS3oFdzi38965nfcGhreteAoALds/\n7jdVs4Q2giAIouYyuroxweLEyntTjP4BTwCvBo2eOu6ofte96Nz2ooHmoKjvPvMF8G6I6auf\nX/ipfhM8LnHI7VTn37IRUV+YpYcxy5SkhwOL8wBMWmv5kokFaam5inh7XOCR5Tn+bR9Lf9kD\nDZo8XXasCVClAeXcNY14z9rV7P0VOU8eS1iAF+oeAFivb6Rcsbd2XDGAxUds1Bdcm/9N3g0X\n4u1FIyo1jczMTADPPFO1yN+2bVsAN2/edNkx1SWYd+P05vsAhsypZrVgudbwqAQQYfZZjFne\nypypYQHWiBE9pPT1AQUA+k0R3vgpezcYjvtvrl5l4FYMLu69C0BwB5sw1Qggcm+VBnRu+z0A\n3NXDyhPuEhHiQjT0bshhlUkkP7taJM3+RXnNFFfLJIdpL+69C6hdVKnFyAXoEARBEMqwRZfP\nz7YA0Nh8sWCDfQMi7wargxVUpK9Ia3JuexH/EX6RB8dbIzsKHmFhE74tyiGTamE/zLvx3hQj\nAFa4LsbiKV4QXaHGu8EYHRNwdtu98oeeDZooqUtjV9iSv879aJjAAWDKhhYAWE55sHw/PYek\n4dcG+N6NU5vuQ2pOh3BDvOuAg8Oj0j3ra23Fw8MDgOBNST4o9/Ra9g1xCUzgKM6vx3f9SQoc\nKuEaN1loQmWFB9O8nRCaZTbMW3HTtXJUCYDlx6RNmzYIHHd/8AEQvqV50rx8ACx1dWt4IYBZ\nO102ven+nN+VCeDJY0+IekmUU7u2RxQC6PhCqdFQH8CYZdKbHYrJhdQqxP5FeTAna8ghTn2T\n/H0gCIIgCJXwBY5lqU00LOrmBI644JJO/68MwBg7eta1EjgS5xQACNtcdUkpkCSUBQ45uCA2\nwaspIJ5T/su5nwH8eaCqHJO/nP0ZwJ8HWR16wm+QURY45K4xbLZCCyCBowbxWnulmj9r+eKu\nOy7O1TYHh1Uw4YPQnCFznjlsru/iUM61ZtVcXLyTmEelnsmxBv/2ANBvSntBaBYLZRRUVGjI\n4ZhcWKo1kWvi2B5R8KjME+ZT7A83TMKH5K0sX9pgDIxou3N2gY3HXasRWzfZfNAzHR7KZYxZ\nxYCwdnGjTcsjCr8ALBWF76x55nlTMigXHKOwFyZ4PS33GDSTpI1qXE1NB+DhVakml54gCKLW\noNB7ohyNwbSMXsHVHjy9+f6QOc/YKXaEWVNVtnpMMYCl8tEMkmETkqiXGAAkhBgBPPdi6dlt\npSznvkHDysa+Tw4szp20tur0vX5iEYCFB6VHicGbUxbv92R8FoBhUXY5DVeOLAGw/LhSdolV\nMCt0cLQrxQWSNmoQlMFRyxGbNUjy0ApxYhODf8snebo9sjwH5mgMjmGRbVhiExea4LTCM+bd\nYPe3KpHzbnBUViBhqvG5l0oBDIqQEGUERTPh5kFQfmOuVd4N2xYxajoD5H9Jxi5vtWt2wa7Z\nBZJDthHbVX1vmXeDCRwAtoUXPvOLhwCGR7UHcGR5TgMfPCrzZMsm2T83BDBxTUuWSFpSWFP7\na5zPtRO3IRP5RhAEURM5vi4bwEjHTGcwkmMNgI9PU+kRiW3hhQBmWm8CjU6plijhOBYPegAg\noDVgtlX6t3kMYHR0AHf1yPduSBKxo9mBxVZcv9mMeE5Z7N04FJ0LmfEQi94NOc1rWWoT1lzz\nuEzinpXvcJHzh9rv3eA4ujIH9ll7COfg5Vn7hxXqtMBBuA9y3g2uk1VuiNRsiquSCSRbyixG\nKuyJygcQEi+9qqDs3WDINXH4NK7waVzBnfzmJvmxORRHI1moXpsQx261bv8I8hVxNhCdbFqA\nYr8AkleEzLuxd36ej9nDcTgmt/yxR/2GADB1QwsmcHA08n0K4Em5J4BTm+4PnVv1O8ka7M0p\n6HWdnkEm6ZMJHADiJxcBiNovu+xGEARRC1BYwlGuNTmxMQvAcN5QLZeGpuDdYJZbuXUpG+B7\nN66kpgPoFWSjc4R5KJjAIeby0XQAvceYXjxyD1vFqbaWw/duMDjvRspqA8xtaCqx07vB4Hs3\nTm++f++nhgCaNnsKYHxsSzUDyPyBZQY/rJQJQP/9zM/b2/6DJWohXu44U6IxtU3g0Ol0er3e\n1UdBCOFkXb5dX/J0K/BuOA6tZlMVqsh2zCwA0LCR8HHzHIqsn0JQNKMe8TmPIefdkIwNq7lY\n9cujJiBdJalrDE2ao6TAi/UKMb/P44eeTNXqN7V92oasxs2ejFhQdWE0IbYl0+YIi5B3gyCI\nWoZDvRvmEhCl+3YbvBuMi/vuQiro3WbkLsbWnq26eBLYKtVfuYkz78WxHSrh+lzZAX99wR+i\nBpPP9D8DeEMnbcfwCyhX3kW9+hXJsQagvtSXqpbcd84qqMdTLrj1vEv77wDoO7mqHq7jiyUA\nAM2udpQh70ZNwcvVIyo3b97s0qWLQ1MvNRM4vvrqq0OHDiUmJup0Op1ON2jQoIAAif+t6vM+\nbaN79+56vd5gMHB7NxgMAOLj4x20R8KhDJcRy89uuwdg0My24qk/gXeDYTErW+Dd0DDUQ7lg\nRTBPKxhOUUA5yTI51tDY18bsdDWwtFfJmtVajPIVITe1K/b7lBRU/aflrrSGzn3mzNb7Z7be\nHzzL9Ah5N5Qh7wZBEIQCzLuxy8rcLg29G2I478aascUA2v/ioZo9xgWXwDQOI405m8OuNSo5\n78bJ97IADJvXRtIRzGd3ZP60BNPV44yeD4Pnmh7/YHcmgP7m1nmmiQyeJXG0fPElKSofQIvW\nFnQQAE/KPcK3Nl8xogTAirRq36VdcwoA3+mbm/cKxqX9xRZfiqiDeHm4ckTFaDRGRUU5ei/a\nCBypqanBwaZQI71er9frQ0NDv/nmm27dumny+urp3LkzgPv373MCx/379wG0a+ekyAZCEoGs\na7HLSiHVmdWaMP//0iGlAFaflmhO3RRiBDB3j1Ap4DI+BCsAOyIKoK5rVoCkd4MxY1u1V1M4\nWgGLBj4AsO5cI3GpigLJsYYmzawTNdzWu7Gg/wMAGz4QuV/ckqAlVnzPlWvtCYIgiNoHS4IQ\nj0vYD7srDuUt0mjoTxTDvBsa9rPY8yJpG7IA8N2RDG7omL+6wLDBu8GYaL4i5ZJcd0fmC7ZJ\n2dR2x9WG3Kf6nZm68HYASvNtmRVh3+R7/9cIwJPyqpcN3yp8C+ZZm2dhEjhM8N0cGmLzBTPh\nJrh2RGXv3r1OGLbQQOD49ttvmbrx5ZdfvvLKK+yRxMTEl156yfkaR5cuXQDExMRs3749MDAw\nIyMjJiYGwO9+9ztnHgYB4NyOewAGzpA1QWyYWARggXyQtQIsH/vrq6Uv/KE0ZXWpVSOUAtgp\nBKh2GI/LHPvXz2ZJGvk+bfdctQAw5t1gAocyYu/GV9d9AYyOAYDW5iIPZZLjDABGq2hNF1DX\nvBt20riZdMwb590g1PDxkQwAb4+T7VoiCIKo3WydXghg1q5qErlP46dHluc4bcJ3zZhiwP9P\nA/KTVxlGLwuAuoCkJe8zi6KsUZFlyRcX1JuW4M/3buxbkAfgQYkXeD5Kls2RtqHqUsd+zYV7\nF8PmtflM//Nn+p/fDTENm0iWuTDvxsmELADDItvwpY3+09rpd1Yr+1NDqEwM3MMHXgDqeQur\nPQXeDcZ0WxUcou7gwhGVr776ygn2DWgicFy+fBnAlStXmLoBoFu3brt27frd737nfI2jc+fO\nYWFhiYmJfHEoOjqaOTsIN4F5N5jAIYlCqvMIXnrW6tONU1aXSm4m9m4w5K4AHCRFH4rJhblu\nQ+zd8PKSNomtO2dyLih7N2KDSwDEyHs4tUIu2sNx1BTvhg2Qd8NOriSnA+g1WoOVQ4IgCOdg\nj3eDtZ9GylzVhMb7M2uqJCypPeP/fAD4ty4HMHF1y48OZwB4Z7xdYnH2Dy7rAuO0DLF3g8EN\nHTtO9Fk3vgjAosO+D4zSN4vMu8HIutVQchtlOLGGZcYrXBjbs8hnA1pdML+/IgfA2BWU3OFs\nPF1UGWowGF599dXo6Oi4uDhH70sDgYMpMT179hQ8HhIS0rRpU+drHOvWrevevXtycrJer9fp\ndKNHjw4KCnLa3gkOBe8GY8FB39Q1htQ1D61y+Ato1uaxzc9lcKcQfqrFsHnWBWWLy7E+SGLi\nfQPJ7Vu0fcxMIvy0c0nY2sWUDbJBHvsW5gGYsr7F1stVZ1CVixg2eDcIG1CYYyLUw7wbTOAg\nCIJwEySX9x3B+glFgOfCQ0KjhNO8G4wlR4VeDE0CkuQGbLnrnysp6VdSjHKXN5KPW7QS82ni\nW+W15EcFvr8ip1Vbifvw14bmfXomT31p7vVTtwD8+Fdf5Wg2Br9TlqvD48JBxNtrODRE1Ho8\nXVQTu23bNgAzZ86sGQKHAkxZcLLG4efnFxQURKJGXeDYumzAe5Qj08gFiNvU0jZkN2z6xDcA\nRQaJ4OsJsS13zCzYMbNAkMcBoL5PhbUyioCYlCZrxjgjQcqZ3g2ibqJ+QJ15N+hijiCIOkL5\nY+F6K2sf7zfVcpXJ8Kg2700xAvj1G0Uwj5fa6d2QRNxXYieCCHZ7YC2zEdubHVmWA2Dcqlas\nxUyQUs/Fc2ydXvjbfhZec9Fh30/P5AGorJBeDedX7IXG+3946I59b6IWQt4NV3Hi26pQmFG/\ntRxqy+fYP6sFyqh/ul6vj4uL+/LLLyVLSDRHA4GDNbMajUY/Pwn1OigoKDMz86WXXkpPTw8M\npNlpohr2eDcEqBkB5SPOphKnWnB1YmoWAQRBqv1D2wFYO64YqOfb7IlgY/XShoJ3g5F9t/6S\no1q2b1iMgCUIgiAIguFo7wZn2RB7NzSBi+SU/Kr4YolvLrh24jakyrzZ6ovkxcm28EIAv/qz\nEVaK1NzG6p/FLtvip1QbiD62NttbNDJybF02AG7B7J8X/bmgk7ErWm2PKNweUcjaalm26LQE\n/zcHdwLw5mC1x/+wuN7/vhD+BLnke/Zp0rz8Tr8tATAhTuKOSeHSkeR+Qj3Bv69SJTytjBzk\nP1f90zMyMgYOHBgfH8/FWTgazQSOf/zjH+IpFca8efN+/PHHiIiIvXv32r87oobCchza/OJh\nWbEXeGcRe1B+EdaUVv7YgztLKQ+yqkHcbTZigfAY2Mmbi8J69rmHNu/OIsrSxoHFeQACOj2E\nWXDhsCrh1WJTGkHYibUD6h4umiAlCIJwBFYtLajxbnDM28euefyupKRfSUmXuBOu3hkpSN1q\nEfhIzV5UejeupKT/6s/4718sX4Zp4t0QMG5Vq2NrsyHybggQZLjahmA0tc+EZwH87wvj7/rm\nf3o6/80hnezfBUHYhqfTa2LXrl2r0+mmTp3qtD1qIHD06dMHQK9evbgWFTErV65s3bq1M98Y\n4f6w2+9Jay3PIkrCOtVffL0IwIDp7awdAfXyVvrzNud3mvyKyt6NxLn5rdo/RnVxPW1D1ogF\nbRYfafr+iocAzu24p3IK1OVkZUjnhqiEZgcIJ9AziH7BCIKou1zcdxfm0lY5FCIbJNkxswCA\nt8S4bTWYd4PRY3hHSd1EefVl5s5mgFoRQVAcw9VpcR5bi68Qta/q+nDU4tbJsYbkWAM/8mPB\ngAdA05e7F53YmCVORju4NLeJHyauNr3raQn+x9ZmH1ubPWqx1Qt17Z8vM/zoU79RBXdNKMhP\nCX3PH7AwFMz3FB+OyQUwPlb7+mGiFuPkFpU9e/YkJiZ+8803kqMeDkIDgSMwMDAlJSU4OPjV\nV18FUFkpcd8YEBDwzTffvPTSS/bvjqihOD/HQVwFX1xk+9+0mrTns1vvAZi5sy2zdDLGrmjF\nzkYMyclPZcwN5xILO+JwUz6S4tGl/XcBLDgoe1Ukdtuq8W4oHCRBEARBEJBfA3DCWKjcwkPI\nxmqXCn4tq1nQv//SF0BPjXLtHLT4wRdB5KZmnAA3vQLzIpy5HFcaa2er7YT5msXXxkRdw8kt\nKqGhoQDEIoCHhwdkdAP70SZkNCgo6LXXXvvwww/55awCunXrlp2dffbsWfY+ibqA8k2vzd4N\nhsVOdWWUJQa57lXJMI6wTSbthgkcqD6tKt6+dkPeDcLRfPx+BoC3x1KoE0EQbodz/kEpezcY\narwbB5fmAiZ7gjgNHXbcD9sgNHx48A6A/37hyy20vL8ix791tbUlVqcFdd4NScR1LRvOs2Z6\n6X56zrvBYZV3IznWwO10+Pw24vDUM1vuAxg8W9W61+ZpRqBxbk69r6+Uxp1qrOzdIEctIYm1\nuRs1Ec1aVAIDA0NCQkJCQhS2CQgIsLgNQTiO5cdk27xWjykGsPRoUwBxwSUAoqtrHOz8ygSO\nw8tyxGEcAAbNsiBknNtxr159q/UOBVuEnHdDgb6TLV8Vnd12D8CgmVYcJ3k3CJdwIekugHdD\nKSCGIAj3hVs8d+3dpprBFgUkb5id8462RxQAiNguLWrwUzOUJRVxZqpFzu/OBFCc6y24zhGX\nfzHvBkPOu9HpNw8AsAkd5t04s6VU/cGo53xipk91P66CVrVhUhGABQcse0nO78oEMGB6O4tb\nqiFxTkGuoR6A6GQtw/IJZZxcEyv2aDjUu8FwbE2sRZzwDgkXYvGml6VX/vRNk4qnANCoSYVc\njrcz+WOfAgCS3eYDZ7RlTbEADizJBTBpTY0ZfVSY1eSGWp19TARhPeTdIAjCbalZ/6A4e8Lm\nUCOAOUl+107eBtBjWEcAXK+q5HOVMz4y/tUYAIZbcTB9Jj4LoM/EqkdsaBKNHlIKIO50Y4Vt\nprz+CMC+z+1KHONzZsv9zB8bwtwaG7GjSnARG0YEcN6NtA3ZkIqu5zNntxUpBmXGegPCtFEi\niNqEk0dUXIKLBQ6ijlNWJPsbKMjxFiBoKlHPxf13AfSb3J6FaXGGzKWiQKyjqwwAxiwznZk2\nTi4CMH+/7/hVreb2Kftnn7IXf2/tzqu8G3zDiPuQNC+ffcOt8m4QhJNhNmZ2KUzeDYIg3B+V\ngx78gRFHYNG7cTgmN1A+Lk8Ts8bx9dkQlYxwrB5dDGCpaD1fzrshx/WTtwB0HyYsK2HejUtn\nlaphWG9uQIeHhvSGAELipTUCzruxeZqx46+qfWnnrAIAbPW2deAjfnDpwBlt9y7I27sgb+oG\nu8a0LWJR2uCbWdR4N0wvq5F3g2HznBFhDyRwEITDqXjq0abDo7s/sLOIP8zzig9KJJT1oytz\n/Fo/BgCYuWuqeQAAIABJREFUtPnTW+4DGKJudlElfSY+e3xdtnf9yvLH1f4HNG7ydOesAg9P\nAA1h9m5c2HsXgIeH7Z5Pp6Ewq/n2uECmKBEEQRAEoYYrqekAetlR6nTpwB0AfSc9q3J7bq1F\n9pBEUyTbZxTC7CmQnDFhlXZzkloAOByTm/FNk7JSz+8/z5+W4M+8G6lrDACClgRsDS8EMMu8\ntqSc8SGY5N07Pw/A1I1a3tVzZo3EufkwB6Ix78b1k9W25N4C+9Qq70bCVGPkXgu+idv/bcT3\nVjCBQ5lP0m4DeGtER+4RgXdj46QiAE38ngKYrkII2BRqhFROvBqSovIBhLqBh5pwAp5eLp6c\ncMLoBgkchCsZNq8Ns+TN3SP7H1k85QjAv81jzlthFf3MCRSSYVose+JhiZeHB8CzbwCYv9+X\nO2N17vpQzclGYSmG824o16A4E+fX3BCEbTDvBkEQRC3Dcd6NdeOLAfyxn+wGl49m9B4TyBZC\nWBuIMlx2prWOWr53Q+ClhZR3Q4Ff/Z6lV1RNo5xMyAIwLLKNwLuRn+Vt8dWY+vO3i/6/frUM\ngG56O3b9CXj95dzPAP488BeSTxSPjYRvVbpE9G4gfXe3clQJpNLimvg9ObI8R1AoaxXi7zOs\nDCIhahPk4CAIh8MU69TVBgAPir0ATF4XcGn/Hf92j/pOFt7GiIUAe7wbqWuyAQQtkfBJjlwk\n8eCzXR/c+MQPwPLjVaefd6eaFJPN04wA2gQ+CpJPHnHbLlX9zkwAunCa1SQIgiAIVSh4N64e\nSwfQc5QFc4d67waD793YMLEIwIKD1dwc4ikS5t1gAofkjMmktS0uH83gP8IPywTP+DDL+rlg\nDm29GwK4Mjs7+c+nfo0aPxU8GLnX7y/n8sQbM99rA58KAJm3GixRHDrevygPwOR1Vd+EH77y\n5RaWtkcUAhDclM03j40cWZ6jfNiHonMBzE2yoI6dSsgCMDRSqGscjM6t3xAT45ydKKec4UI4\nDg8SOAhCc/jz85IcX58N1PdtxUZRhN4NhyLInti3IA/AFFvnJLmlmINLcgFMrB5Har40cb13\nQw2HYnIBTFAsJCMIZ8KuyJ+We4A8HQRBEDzYv8feY6riThcdVrr95rZkq00K6zQcXHam2Lsh\nqbxAFG0GGS8tn63TC1G9JKXaMUSbXmrp4FIAq880Hia6e2coWyoYvYI7/OfTQgC66e3MMRmm\nax4574ZKTsRnAVUWEsGl1OZpRsCjnnelXNOfPd4NBvs+M4GDwYZ62Ep+fel6XKLW4uQWFZdA\nAgfhFvDPpiyASmzfUEYwGspHUl8AELSk9f5FefsX5fE1dQV009vd/b8CyFg/JKOt2Xgt4MM+\n5Xs3fvWnYgCA2mAnhyLp3eCsnk4/HIIgCIKowVj0btiPWEFQiWQShzLKQoO1sPAyzgBrFXJX\ndJqg8AZZ8ii/6U9usHfJ4FIAa85Uq3HxbVleVux1ZHlOZaUwnQSAb/Mn7IPkOAN4wg2f84mZ\nAP71mS9EHbQT1JkvmHdDPH/kfO8Gg7wbrsLT09VH4HhI4CCcjcW1Vjaf+cHuTAD9p7lyaIJ5\nN7iTwbQEfxaEzgSOZcNKV51snLYxC8CI+cJ/0+9NMQL49RuAw87ENsPsjsprAjtnFQANWnc0\nJY2Td4NwN/iLkwRBEK7CDXvWe48J3BRq/O660drISTXeDT6ShXd85YUvplgVnXZsXTaAWbtM\nK0lsiCNiu7QAsfqMsBSWyzT99PQtAG8OEdapKKOm4kS/KxOATkWryPCoNpCfNJmz248NsChw\nbsc9wMPTq7JZiycWd6cGrYZ6iJqISwQOo9F4/Pjx0NBQAElJSX369AkMdOD/TBI4iJrEma33\nM39oCClbo8JoqIK+wHk39i3MAzBlvdIpjUvQCFrSetmwUjUHzMZrJVdL+FVbs99+CGDLxw33\nLsiDujOr/chJM3zIvkEQBEHUYmywMziCwzG5UCw7s5/Y4BIAMSlNBG/25HtZ9281BPDLPxTL\nLUFp5d1g2ObdAHBgSS7MHXYA9DvvAdCFWy62l5uaUU+I6oaRNSK1BcDyEaVAo15jDAAA4fIS\ndy0q6d3gGBDWbudsy/0sykxL8P/8wk+fXyh4/d3nbHg6M5JY7KAl3BmXhIyOGzdOr9ezj5nM\nkZ6e7jiNgwQOwk3hezeOrsxp2qIcAFDtj5IL8WafRg8pbfvsY8gMW9p8evNr+QRA0JIAJnAw\nVp00ncAkBYJdswsaNUGzgHJr9+UcmHeDCRx8mPPTp+lTAOFbpTvqCYIgCILg4LwbXFjVR4cz\nALwz3mWejpihpUC92FPCO22j0Svq3bL4Cz42vOanZ24BeHNwNSuEQv+a2XPRAYqlcgLE6SEM\n5t1gAocCLG5zQlxLLtP0579XTXNc2n/HcMsHPCFp+fBSACtPSEgSFlHj3TDt98CdRo2qrDRr\nxxUDWHxEIhWFK74VPD5whknECd9iOUyEY0tYIYDZiVqKU0QtwMPD2Rkcqamper0+KSkpJCSE\nfRocHJyWljZv3jwH7ZEEDqImMXiW7Z0pykxZ3+JKSvqVlBLBysYHuzObtjCpLQrtJ+vGFwFY\ndFhaPbG4NLTl44bsA+d4Nxgj5reJGVb6r2GlsSdtObUThNty+Wg6gN5jXLwkSxCE++Ny7wbD\nod4NRkxKk6h3ywQPXklJb9aWS0NoBuBqajqAnvIFMS5kUnVDLvNuyJWD8Kmo8HipR6GaXWib\nNgLTFI/pKovJQ5+fLFb/dMFszvWTtwAISnCtxTbvBoN5N9SP5xBuiPNHVJKTkwGMHDmSfRoU\nFBQcHBwVFUUCB1F32RpeCHiLC2LB824wnutaBvP5T1z6bZV3g3kZWpkTKI6tzQYwarFaU8N0\ndRL7uR33wBPmXY67ZYUQBEEQRE2BC6uy07shcKeq5NrJWwB6DOsEQOzdYNjm3WAIvBsWidje\nbO244rXjihcfaarg3dgdmV9Z4QEgbHNz8LwboxbZYiMVx22yXa8bX/zNtWZclQwXHaLg3ZC8\nQlNjRRFffwJYeKjqElTSu8EQezfsQeDdUAjjt7M00AZYnwAL3SOcjIfTBY5z586JH9TpdI7b\nIwkcRM1j8zQjpFpLJll5f85larBPi3PqS2zkgf6h7ZjAoYCcd8MdEEd/c5B3g6iVkHeDIAhl\n3DAZ1CX88HVTAL2Cqx7hezc+PHAHQJ9JEsEc19JuA+gxoiP3CHdtZm2sSfnjquljNSHoApS9\nGwzlllwOgX3jxjXfG9dKVqRJt7ee350JYMC0dgB2zS4AUFEhsZnCFI8aBLmqCt4NZ6bJkHej\nRuPp9BEVAampqQDmzJnjuF2QwEG4OwrpoQpYLFc/vj5bkOjBR+BlEHg35vUtA/DeJdtXQhju\n490gCIIgCMIdsNa7weghdesrGWnhtDthBasCx7QE/23hSsMjXKyG+Es2vxE1osPAGW1vXCsR\nPKgmRsTi9aerULicdqZ3g0HeDRfCbx2KGaqqM4FD4A6z9ulfffXVq6++CiAlJaVnz55WPdcq\nNBM4rl69+sknn8TFxQGIjo4ePnx4t27dLD6rstLFGhLh5ggSktI2ZAHo+OuKwbOskwZig0oA\nxKRWk+G96lXy/8P+398lRHrWETstoQb3aamP/iaIWsPl9zMA9B5b15dnCYKQRHPvxsW9dwH0\ns7UfRBkFJ4UNsJEQRocXHgAApK8TuD2+vzIHwFjesDDzbvAlhl+8yG51/ASKg+TIBoPpLzN3\nVv0srPJuaAJ/6kQQvSHn3WAU3q+y/U7f0nx3ZL6nF6Yl+K8eUwxg6dEqcefoKgOsbMm1gTIj\nLVoTquAXQXpY2agiKJG09ukPHjyIj4+/fv16cHBw48aNHTelos0fQ0xMDJM2GHFxcXFxcSkp\nKUFBQZq8PkFoAv9MLBaPVToYoYV3gyAIgiCIusO2GYUAZu5waqWFuI5EPQkhRgCRe4TjwNbi\nXd+0lnnyvSyACzStgkvEkHy6pHeDYbMJRZO3NnaFSYvZtzAPgIIpmEN9m4wy22cUAoiw73dJ\nq4MhahzOz+Dg6NmzZ8+ePefNm7dnz56BAwdeuXLFQT4ODQSOq1evxsXF6XS6+Pj4zp07A/j2\n229jYmKCg4O7du2qxsdBEHIIEpJGLLA8aSkJ825cSclT3iw5zgBgdHTA4WU5AMavaiX2bsQM\nKwWlVxCEm/HR4QzvhhUA3hrZEeTdIAjCuaj3blw7cRtAj+Ed1b+4pHdjw6QiAAsOWB0Bxlc9\n+k2xcNjMZDF2ufR/1F7BHa6kpF9JSe8V3EE8dbslzAjAR+Zy6fqpW94+6D60U9KNfNXHbh3z\n+z8AsPGDRgDSNmYBGDG/2mXkpf132jyHvpM1sMZwl4vMu8E3ICt4N46uzAHAcvQlj1A9rN+E\ng8kuU9a34O9CgfdX5ICn2jgIhVQ4wml4erp+fmLkyJGhoaGbN292X4Hjk08+AcCpGwC6desW\nGxur1+svX75MAgfhZLjGE3GLVXlZlWip35kJ4Lre/80BBaA4DIIgCIIgHIaTvRsMhZQKwX2v\nYMv1E4oAjya+T7//qmmXV6yoNRXDWTPE3g3BBgLObLn//Y3GAP7Qt0CNWYOZ51dJrT+9N9UY\n2KXq08g9fvsX5e1flNe2s8VXtcyU9RYCLI6tywYwalHrZ19g9n57TRMRO5qd35V5flfpAKmw\nT9b/AjSQezqr6bmfbp0ZecPEIljZSEi4J9bOlTgCPz8/AHq93kGvr4HAwYZTOHWDwXSN69ev\nO67hliAYh2Jywatn4/PdZ77ffVa85Kja2ZPR0QEAFg96ADRee7aR4KvMzhd7UrgjptM/eeIx\nb68Fu2NY90cAEq/LnnUIgrANi72MzkyYJwiiFiPZwPLR4QyoK4i1yruhwO/fKQAASN9wbo8o\nAND1dUCkZXx06A4AoCH7lM1rdJNfRuUcHztnFwAI31ItTYP7p8p6aj8/1RJmF8PsRKWLou5D\nZQtBWGxHu+dNn355rgXX87Ip1PjiW4VQ98/8naBcAEAggIzvfcQXacW53hZfRCXxk4sARO03\n/TgEBmSG+Jh9W5X/8M/GCSHGyD1+Nns3GJf23wGq3Cic7GLRu8FQ8G7smc8sNhrcM5J3wx3w\ndLrAMXDgQL1eX1hYyHQNAAaDAUBYWJiD9ujYQBrHCTMEweeHf/tEDyll/eFc44luervvPqu2\n7MB3IX7/96YAXullfFzmee/nhlvDC1m+9OZpxvaBFQB2znoUvtWuKOzDMbkAxkspLwRBaMj5\nXZkAJNeyFPgk7TaAt3g1hwRBEBpyYHEugElrXXYZwO6omcAhQPK+lwunWHjI19GicMJUI4BI\n+ZWhwbOfARAXXK3KpG2nh7m3G7bs+FC8Pefd2LcoD8CUdS04EUosbUxe10LyDQoeFOhW5xMz\nIRoG4bNvQR5EpSSjFpmuS9m3l91ezknyA6ALb8setBbufCcI44fZkqwwDGJbTY+d3g0ygLgP\nHk4fURk9erRerz9+/HhISAgAo9F45MgRACNGjHDQHilxl6jxVDzFc13LfvqfhNduydGmKasN\nKavLgpdW+2++Jyq/TYen3Kct2z5W3sWuOQUApm+WvkZ5obsR6i4CyLtBEK6CvBsEQWiCZAOL\nGu+GtuTebgh5b1rE9qoVGoGW8c6EapET3XqaWlp7BXf49qrSzbbAuyGA9dT2GGbxwC3DVa4c\nis599jmJnFHu/R5fnw11naNrxxUD+H+/Lxky55l144sB/LGfjYfHNIUOvyoDMHjWM+xB5t1g\nAgfjg6S7AMqM9YYrWjPsj3FlsDW8D5IyAfQPtU7uF7N1eiHMtTIhG+2yXbB4XcB1yZZEdZzv\n4AgKCkpOTg4NDQ0NDeUejI6OdlxTLAkcRC2hy4sPANngz+PrsyXPf8Oj2sCcLcqYs9sPZvOF\nSuRunNR4N1JWGwAI9BeCIKzCWu8Gg7wbBEHYg0WPgwu9G2J2z80HMG2T9M0qexf8m23B+zqw\nJBfApDXWvaPrp25BZhpFwbshYOWoEgDLjzUBz6WrwJR1SqEYGycVscYTyR+c4EGBbsW8G0zg\n4ODiPCHybgiI3OP3we5MAP2n2StAcEjOwsDSMIhtP017IO+G++CSFpVz586lpqYmJyfr9fqw\nsLARI0Y4Tt0ACRxELWDSmpZHlkvYLxnBSwOYwM9H8H+fRW/wEWgT0zfbNatCEIS7IRhXJgii\n1rM9ohBAxHbNwj4rnWj0ProqB8CYZVVeDOVbffUDJla523bPzZ+2yV+5cYPzSgDwN+sD3MRE\n6ppsAEFLhAcvOOALe+4CmBDXHmaBQxKj6hCNxUdYHFvTjZOKvLwqX+pZ+PGRAkkzjkXMmoKF\nX6T+oUoNNRaVDosLYIeicyGq0bXfu8Fg3g1NcEm8LqGAh4drWlSCgoKCgoKcsy8SOIjawLiV\nShFKaryLCsRPKQIQtc8h2jN5NwjCaTDD8N8vNwPwp76uPhqCIOoYy0eUAFiZ1sSeF9m7IA9A\n0+ZPmMBxcEkuAL+AcgBD5jwj2JgVuLKPxd4NZl8Vr/FIMmlNy91z8//zXaNZvR++/Ibao03/\nZ1MAGKp2e0mYd0OZxQMfAFh7rhEsJaDNP+AL4OMjhZJflWP9hCIACw9JXAryW1QUYu8Zdno3\nNk8zwuw1FqO+hJXzbuyOzM/OrA+gQcMKyXdH1D4868C0kGYCh4dM54zk45XOFL2JusfiQQ8A\niGtQHAS72piq6EskCMKtIO8GQdQ1NPRuMHqPdl6yD9+7wUialy9XsHp8XTbQsEUHiSROqxC4\nKqZt8p/V+yEUGzcA+Pk/gVlt4QZ+ubtusXcDwKenb3k3xJtDOgEwWzyU7A8ctsVDzH+3DGi1\n8YJPzLBSALEnGwOI7FsGIOGSz5awwjYdHqG6R6Ztp0cATiXcHxr5DIADi/MATFpbdeHHXur5\nX6k9BoHSwZzI/OU6iwtg4mgSZahKjGC4Q02soyEHB1HbOLAkF7BC2jj5Xhbk69nhMO8GQRBO\nhhmG+4da3JAgCMIEP2HBTuz0bjDYakrSPNbciYm8GAXBHazFW1nOu/Hx+xkA3h5reV7jt38s\nBcBVzFpEIYxMXP/ByLlrSxw7825Y3Cmfq6npb43AJ2ka5FB8ePDOGzp8pvc/d7YZgAmxwg0U\nVsKOrWVj1J7eDSpT1xiClgSwR8QjSGkbsto9hxELZK9XbShhnZZgesp7U43vTTWK62YYNNRZ\nm3B+i4rz0UDgIDsG4W50/vUDZyYnkXeDINyNDw/eAdBnIl2NEQThMhRyPc9uuwdg0My2cs9V\nTgyR824wDHe0qWyTlEjq1a84sjxHYTRYuWwF5rEa/j0I826oYc3YYgBL3m8KQL8rE4DO+pDp\njRd8AFxNBcz2DQAJl0xlfJKxneOqm1b43g2O2JONh734RM0BiAdexq1slbrGIP8MDRD8NC1O\n0zh0QJtwIa5ycFy9ejUtLS0xMVGn040ePbpfv35+ftpUCIkhBwdR2xBIGztnFTzb9QHkT4HM\nu3F4WQ6A8auUXJcEQdRorh5LB9BzFBl0CcJN0dAuoRWuPRh+iIYyu2YXAL7TtzQHECEf6yjp\nDnh7bODx9dnH12cHPFcGoMfwjoJnfXL8NoC3RnactLYFF+tuZ2grpyOwGlEuinLmzmYQ3V0f\nXJoLYOLqahd4/7jqB0A33bb9S2fEMpUhaInafDSBmH7yX9I3VrlZ9cUPntl6D8CoxSaRi9up\n4Kezc1YBgPCtzRW8G/Yj591g2O/dEM/gEK7C+TWxAFJTU4ODg9nHer1er9frdLojR444SONw\nqsBx8+bNGzduBAcHk+mDcD7vr8hRnholCKLWQN4NgiC0hbvPVP8UsXdj/+I8AB1eLGnSsmpF\nnZMP+Ft2fd1o+7E6EvX3qHIdsRNVe2zFPhfm3bCWI8tyAIxz/CLW2e33AAyKaAtgd2Q+zDMg\niw6bDnvHzAIACw81B3BmawnMpTPmkhfnMSG25amE+1yqiBhl74Zykivhzjh/RCUjIyM4ODgp\nKWnkyJFM0bh69WqvXr2OHz8eEhLiiD06Q+AwGo2ffvrphQsXEhMTAeh0OifslCAY4VubA81Z\npZkC5N0giFoPeTcIwplIrroro4ld4tKBOwD6TqoNKiffvpE4Nx9AmNTAC4DplgZDGHLlsi0C\nH0LKu8EQiC+fnvkZQMT2Xwg2+/DQHQB9Jgi/83JKh8C7wYfdXZ/dZiqILSqo16LN46OrDGOW\nBcCcNrIireqbs3d+HoCpG634/eG+txsmFtWrVwkgcq+feu8GgxPTzydmAgCUFsffm2IEPBs2\nqmCfDp7VFsD/xhXzt/nLuZ8B/Hlg1fdWUlNL25ANYMQCyy2BCnNSAuz3MjP5ZsY2iQMm74b7\n4OH0FpW0tDQAfC2jZ8+eAEJDQ2ukwPHtt9+eOHEiLi6OfRodHT18+PBu3bo5dKcEAdF1FXk3\nCIIgCIKwGe4+0x6z/WRTdkO1+3CBfMCwv+1CkDkq6BAVGAf4u9s4qQjmOlX17F+U165rtUcE\nisbRlTkAxixX9X07vfk+gCFzTN4N/4DyygoPwcqzxZB4mNNYQ9+rttP3phgBzNtnuzd+5cgS\nAMuPC1NjmXeD0aLtY8nnfv8/n1m9H269bApqXXykaera7D1R5bApJVQSNuDz294Fph+rB3ZH\n5nN5ohxy3o2Le+8C6DdVqciGvBs1F+dncMybN2/evHnO3KNDBA5uFIV7JCUl5bXXXgsMtJzP\nTBDOZ8/8fNhaNkYQRG3i8tEMAL3H0NmqDnHtxG3Ir13XYhxdG2mVd8MGYoNKAMSkCm8y7fdu\nJEw1AohUTCVwPnLeDca1k7cA9BimNq3THt4cLPRuMDL/12jyOgkPBVM6jn4n9NJKejcEMN9K\nEz8AYPYNmH9pmcDBsMq7IWDBQVVSzvYZhQD8WpZLalsDwoRBb/d+rtY1cyI+q0NXDI9qw6p2\nOfZE5TflHTvfu8GQHAYRezf4EzF8pm3yZ18SsGt2AXjGn/2L8gBPyR+feiS9G4S74ekGLSpG\noxFAdHS0g15fS4FDMIoCID4+PioqCkBQUJCGOyIIizj6uoogCMexc3YBVETxa87KkSWvD8Lf\nLjb/28XixTZNehMEUaNhmZGA0h2vSu/G5eR0AL1HV+lHcsMC4i2tZVt44W96CR8USFcCd4BC\n6IO13g2G5L3x/kV53JdUejcYQ+ZUMxeUFnmJt+F7N+TcHJJ1M3zvhm1RqWLvhno47wZHcZ43\n99NZNaoEwLJjdjUKm+MzTD9HseqhDPNu6HfeA6ALly36EXNhz10A74ZIWD9siLAhHARfh2I/\nF/UIfoLWPp3jH//4B4Dhw4fb9nSLaCNwCEZRwsLCJkyY0LVrVz8/PyZwEIQ7Q94NgiAYn5/1\nr1/f9YsbhDOpg94NhuO8G85B7N3QChu8G6vHFANYetQ6YZS5h5SDG1Ty3ZXmrH+kdhA/uQhA\n1H5fOd/K0VUG8DwdHAq+JMGQjrUodNNIMmtnsyPLcy7uuwug35T2w6NM4kvKagOA4KUBAE5t\nut+iHYbOfUacIZIQYgQQucdPbhhEEJlhrYohCG3h9CkbonOImgUz7zCsHVfhP9eGpzMMBsPm\nzZvj4+MdF1uhgcDhYX5zrNW2Z8+eAQHWJfQQBEEQBIfzvRsALr+f8fpA9B5LwykEUXfRsIDp\nyUOh6UAu6FHOu3EoOhfAhDjLt5oWpY33V+YAGGv2UFicxTu+PhvAyIWWMyyVsXPkAUDahiwA\nFhtSxd6NyL5lAJo2qQCw8kRjuScy78aWsELwmmsZa8YWw9beFmZd8W5gecv6PhXnEzMBk62D\neTd2zS4AqkVB7ogoADBju9qTo9gIaa0GZ5V3gyHp3WCQd8N9cH6LCh+j0Th16tRu3bo5NJVD\nsxGVlJQUmkMhCIIgCIIg6hpq7hvF2ZaS7iFuzV+zg3NLjq3LBjBqUevj67IBjFxUTUyJ2m9h\nTEbs3WBw3o3u/QsA3Lhe9d1m3g1+VsX0Hg8B7LomnBnRiuw7DcQDTcy7wRg69xlWvyLOEPFp\nVNHAp2L/ojw5kcjmuhPlEFnneDcOxeQCmEBJpa7A+SGjfOLj49u1axcbG+vQvWgmcAQHBycn\nJ5ODgyAIgqiJkHeDIAgN6TtZSaGQjIRk4xWAyfqhxruhkrHV8y+Uc5SvpKS3CLR3gomFg3rX\nr4R97b8WvRvKKHg3+HDejXM77gEYOKMtbPVuMCava8GmbCzCTydl0sOrw3Kmb3kOZhsIQ9m7\nIQ4iFRshmQZ3KDoXqM89SCn7dRBXCRwGg2H58uUtW7ZcuXKlo/elgcBRWVnJZXDo9XpUz+Cw\n//UJgiAIwirWjCkGsMTKeXiC+PyDnwC83v85Vx8IUQtR2Uta670bjFFmy4bAu6F+MEeZ8kee\nko/zRSX7vRvsaL//rhGANWcb8b/08KHa+0i50lnOuyFXGPRBUiaA/qHCAhdlfvGbBwCUk3Qd\nDXk3XIhLWlS+/fbbl156KTo62tHeDYY2Do5u3bp169YtKiqKa1FhRSrx8fGavD5BEARBuArq\njiWIWsPH72cAeFvRseWEP3nJSEjnx74yn4UgyFOTw2CvOf/dMjUb21Cxwbda2MPpzfcBFOfX\nYxMf9r+gAPVv7eLeuy+8jqyffN4YYLXAKhdEKmZCXMuTCVX1upx34y/nfoZUSS1R+/CQlv4c\niMFgcKa6AW1rYv38/HQ6nU6nmzt37o0bN4KDg7kKldTUVBpdIQiCIJyD07wb107eBtBjWEfn\n7I5wNN997A/Aw/On1/rVOROH5L2utSh0WBB2cvXYbQA9R3V08XGogMvXsOG59ng3kqLyAYTG\n+wPgWkusxarfYf7RrhtfBGDRYZMzIjq5KYB144sFT+FX57Ic0+XHmwK4uLcQwKS1EuM8coVB\n1no3GMMi2wA4siwHwDhbgzyImovzR1TOnj0LIC4ujmtc5aisdIidREuBg6Nz586dO3cOCgri\nRldJ/G4lAAAgAElEQVSCg4MBREdHDx8+3HGVMARBEAShOeTdIIhag7J3g+G2f/L1GlR+eubW\nm4M7ib90/eQtAN2Hmb6kbHDg7uHt0bPem2oMaPcYQCPfp5DqMQGw8YKPmpcK39p86eDSpYNL\nV59RFZkB4O9X/QAMnCG7QdzoYphVhn2L8gB07FYCkXIxZM4zKvfISNuYBWDEfLuSQcTsnZ8H\n+IijRjl2zCwAMGObQ7pIjizPAZqK81BtIznWAGB0DK1quynOb1EJDQ118h4dInBwCEZXmHKj\n0+nOnTvn0P0SBEEQhBMg70Yt43cDcwG83POXrj4QF8C/1901uwDAdOsLm8Xr3u9NNQKYt7dm\nhLK5swOl56iOn565xX26KdQIYG6SO35jmXcjdY0BQNASq290Y4NLAMSkSNsWUuIMAFadlHhZ\n5t1QZt34Iv9WTwCEvie9sdxPn2lGgvZWPsy7IehzXXRYaCfkt6JI5piyNxgc7ViBgLwbdRbn\nOzgcZNNQwLECB0M8uuKEnRIEQRCEJgjuea6l3QbQY0RH1x0R4Sg861dUlHt8/clNT6/KP7zZ\nxdWH40Dc+U6eOLwsB1I9oG8O7hTZt+xMYlnCJZMzgvs5ct4NhnKWhMqfu3J+hLailXrvhkqY\nd4Ph16Ic5nf9t4umcpNdswtskPAE3o2DS3IBTFxjb2SmgneDIendOLExC8Bwu+0kWnk3GOTd\ncHOcn8HhfJwhcHBwoyvO3ClBEARBEIQaSrLrN/Ivd/VROBCVuoYNN35y1BTvBsMlis/XV28C\neLlnZ6uexbwbV1IKlTe7sOcugHdDVDWzqElglUQy0CFoSUDSvPykeflyXgk55LwbDDutDQ0a\nVpYWezX0qVD/lD1R+QBC4i3nj24NLwQ8Zu1sxn9QIbNWUsmy9g3yQz2UObrKAGDMMhIg6jQe\nHi5oUXEyThU4CIIgCKLGIbjnqawAgI8OZ7wz3k0H9Qmb6TGs09+u3ywx1Hf1gTgc8m4o8Ena\nbQBvOdKltXxEKYCVadK2BbF3g4PzbjDs/DleSUm/+5/GkIn2tKrWRD2nNt0H8LTcE8CIBVYE\nke5dkAdg6gbhnbzFMSixx0ErCc+idyNxbj7QxK/lk5TVhuClGisL7H0xgUOO7RGFACK2mzSX\nksJ6uyPzKyo8AEzf3FxuGO3stnsABs3UuFOGcAecP6LifDQQOAYOHKjX6616ivNHcQiCIAjC\nEdSgagNCDX/s3pnd39ZKSNdwMh8eugOgz4RnFbZR9m6krskGELTElkYSqPZuMGzwbjDEgQ7m\niBC7SnkcgQ2pJSEqoj0YnHcjaV4+gJ9uNgQadnr+oeGOtGYqp2SdiM+69Z9GANo//9CiLCLw\nbiwa+ADAunONxFs28KkoKYSXV6WXV2V5eR0YVCCk8KwDP3kNBI7u3btbK3AQBEEQhG24PDvg\nLdIyajsOXbon3B9tfwGatn70xaUfX+v7PP9BOe+GHFY1U3x0OOP66RYAVp+2sBfxP1KrMoZ2\nR+YDmJZgnYoxdK51xSUcAu/GR4czALwzPnDeXr9NocZNoUZHR65uCSsEMDuxmcUt+YRt8mdP\n5GCVKN4NKtWkotoJ592A2fGxd34egOmbm0PeyULejdqMS0dUrl692qtXL0d7HTQQOH77298C\nCAsLe/fdd7t06dK5s3UzhARBEARRc6l44gng6rF0AD1HVd0tfHjwDoA+E5VWbgn35NqJ2w39\nygG88nZdrFORZFOIEcDcPTUpUMPlXNp/x/4XYd4NJnBYy4oRJQBWpClFWjgI96x3Ob4+G8DI\nharsMPZ89wSxI60DH1n19OFR3EyNr+QGa8cVA1h8RKKERdK7AeCjQ3cAvDPhWYuBpkTtxoUj\nKhkZGb169XLCjjQQOF544YWUlJTk5OSBAweyR5KSkn7zm9907drVz88d/7sRBEEQNRfy2BOO\n42pqOoD/fNoMwB+H5GyYVARgwQHpe4waBFsxlixiIBzNlYNtYlJV3SQr/JisaqZ4Z3zgO+OB\nqnQGK27RreqHsta7oS38FCS+nvJB0l0A/UOtmM1RyezEZolzChLnFIRttu5PSTBhRH+JhAtx\n1YhKRkZGRESEc/algcAREBAQFBQUFBR08+bNGzduXL9+PTQ0lH2J2Tq6desWGEhJbARBEERt\n49rJW/Uaokf1gkYGeTdqOq+8/ctPk4tcfRTuAnk3bKDv5Gdjg0oUNnDcwN3BpbkAABfHCUq2\nq9iAxdjXXXMKYB67UECld4Nhp/Nlw8QiAAsOWpBH1Xeg8JH0bsD8c7/1vc/KE6bpJC5G9B3F\nIBiiDuHpghEVNpkSHR3tnFwLLVtUuBbYlStX/vWvf/3666/j4uISExMB6HQ6nU738ssvd+zY\nkWwdBEEQhD18eOAOgD6TnHq5dvloOoDeYyzfilzcdxdAvynarx8SjqZnUAcAXt63AADNaoF3\ng8GtGI9/+TGAw1/X/poYN0Gld4Mht7CfEGIEEGm9xqR827wtvBDATF6naeLcfABhmxzly3B0\ngtKqoBIAy1Kt+997bF02gFGLVGkf1no3tGLnrAI4rNeGqFO4ZESlV69eKSkpQUFBcXFxTtid\nQ2piAwICmKIRFRX1v//97/PPP4+KiuIEm+jo6NjYWEfslyAIgqiDhPd4CGDntYZO2+Mnx28D\neGtkxx7DOl1OTr+cnN57NA3O1B48qjt4//n1/wD89uWurjkaovbioFv9T47f7tANb43s6IgX\ntwrm3eD+YVrcfvuMQl//JwDGx1arX7UY+9rAp8LGQwQAtOzw8EpKeq/gDhf23IWV1TNiVHo3\nAOyZnw94hGzUTFGauFpYW+vT5KlWL07UDlwyopKenu7MeQ6HCBwcfn5+r7zyyiuvvDJ16tSL\nFy8GBwcDiIuLI4GDIAiCsI0PD92Bp4XaRUcg9m4w13Q90Vo4591weeELYQ9/vfrDn3rWqpDR\nLy7+FLYSr/V7zmm/mfQnoAk2eDcsot+Z2fEF6MLbAbiw9y6Ad6e2V+/dsO0ny7ZnYxTiW3E1\nSPrjDi/LreeNJ+Uey6r7ZVROf4xa1Jq9HfthFhh7bq/SNmQBGLHAFDLKtefY5t348MAdwMfm\ngyFqJSMWVJmV0jZmWffc+W34n6p/upPTKhwrcBiNRs7BwT0YHR3t0J0SBEEQdQoF74aD0ube\nGtnxo8MZ3vatGRJuy78/aQbg94NZikEd8m5sCjXCXfsvaiXH1mYDGLXYdIOd8V3jSWtsue0X\n4A7eDT4VT9V64iN2SDewHl6WA2D8qlYACu/XT4kzBEcLU1cnr7WlHOTMlvsABs82KTXMu7Fo\n4API15FYRI13g6Ghd0PA5x/8BACo/8zzZU4e5yTcnBPxVaqEteMq/Ofa8HSn4RCBw2AwcBkc\n7BGdTjd69OiuXbt269bNEXskCIIg6gjO927IUV7myY/xF0ML1zWRz/Q/v/g2/vVx81pm3wDw\nWr/n2AdO+830cFFcP4ADS3IBaKIXOILtMwoBtLIkvX556UcAr/Z9XvMDYN4NxrtTJY5jz/w8\nACEylaJW/QoJNhZ7N95fmQNg7HLLWaT9prRPiRM25o5fJf1Ttja5c5HuAYB1+ipd4+y2ewAG\nzWzLPk1eZQAwelk1beVwTC7MMzX2x5dw3g0Gvz0ncU4BeCEgGycVAZh/wBfm+qfb3zRB9XdN\n0gYhxoX/lp2GlgIH16LCgkUBREdHv/zyy3/6058CAqxotyIIgiAIOeJGFwOITm66fkIRgIWH\nlNbKlL0bHyRlAugf2k5hGzmUpQ2iplN3wvw+PHgH5tIfm70b7EW8vCsB9B4TePn9DAC9x9Lf\niAVGLTZ5xXsFd7h8NOPZXz+ANXnG9rCg/wMAGz6w0aSgLZWWzHDjV7XaHZm/OzJ/WoK/2Lth\nD4NnPyN+UNm7sWd+vth5oXJmZ9/CPABT1reQrH1ZO64Y8g0p7O9LJa/3f079xkSdwsPDBS0q\nTkYDgcNgMFy9ejU5OZnFiOp0uqSkJCpMIQiCIJyJfmcmqi9LOo1rabcB9ODF4F3cexdAP6l1\nUcLNeUP3C/6nn53/CcAbA2rw3QInYTg/EaPnKJeZmNzWu8EQz2L0HiOhB8l5N8x1IXb1mCqT\n/qPzYpu1gvnnh0e1EX8pYaoRQORepRsTzrvx3lQjgHl7/TjvBmP0soA98/MFz2LejSsppeqP\nc1t4oTi8yVrm8zqeWP0TgrBvYd7maUYAc3b7UQIOIYnbzpVoiAYCx7///e/g4OCwsLBz5851\n6dKlc+fO9r8mQRAEQUgSnWxa3VL2bqhBpXdDMsjj+qlbALoP7WTnMRCES1g44AGA9ecbMe+G\nnQhehLwb9uBo7wbDJd4NsRbMGLfS8nDKtAT/KynpV1KKLx4JABB/wXnZmfGTiwBE7feFfGqG\nSh1hyvoWALaFFz557PHTjw3mvFO2+aOqNyLwbnB2RfapeNfrxhcDWHRY2vFBEJJ4eJKDQwX/\n/Oc/ASQmJnKTKRaprKz931mCIAjCcVw6cAdAX96Acb0GEmcW8WaSnN+VCWDAdBvdH+LrdZXe\nDZWHR7iQGu3dYHh6mf40aC231qCVd+PaidsAegzvKP5S3OnG3Mfqe15di8C7sWTwAwBrzjSC\nJe+GgHnmjZkXD7BXTBcwc2czAHPeKeMe2ROVDyAkvkrCOLoyB5Aw0eyYWQBgxjaJGborKekd\nX6r6M6e/d0IScnCo4vr16/a/CEEQBEE4GZX6gmSQh3rvxqlN9wEMnSsx6U24J9ycvKsPRBve\nHhf40aE7Hx268w4voHf9eSsW8K8euw2g56iO7NOP388AUK9BBYC3ROqeO/DxkQwAb49zCyPJ\nhwfugOIeAUhpwRwX9tyFucREDnbH3ivY3sNgYybPdi0F0Hey5Z/Lcy+WAjgc8xjA+NiWChKD\nmF2zCwBM3yKxMd+7IUmnLg/HKAavkneDsAEKGVXFuXPn7H8RgiAIglCPWJXoN6XqyphTLlSa\nI2z2btgJeTcIF7I9ogBAxHbZ+zT3meH/TP8zRPEo7gArQ5ErN3V/JL0bYjT0bnx06A6Ad6wv\nw1II4NwSVghgdqLwp8C8GwCWjygFsDKtMVRjHktpD+D05vvWHq218L0bDDlpQ0FYcYc/VcL9\noZBRgiAIgqgB6HdlAtBZqVNooi9YvAkk70bN4tMzt375Kt4cXHvSVb6++kOzZ/GyHa23nHeD\n8fbYwKup6ZVPPUzRhu6Hm3g3GDXLu5Eca0D1dlIbUFNxJUDZu6Et5jALtZWuQ+ZU+x8+Y1vz\nI8tzjizP4UeHBP++HEDKP7wFz5X0bmjIB7szAfSfZjr3pW3IgqhrliD4uNbB4ZycChI4CIIg\niNqGo50RdBFJ1AIUvBsMVy0IH1yaC2Di6qoaFDf0bjCc5t3Q3E2zcmQJgOXHmwCIn1IEIGqf\nxkkTktjg3WAE/vIhAEDCwdHQx0LNrFXeDQaLFFWGho+ImghlcBAEQRCE+8JdX6r3bnCVmdwj\n53dnAhgwzcYpFXIF1xpm9noIYNuV2uPdYDDvBisW7fD8QwAT4uytUHVb7wZhJ3Z6NxjK3o1D\n0blQ/CU8sTELwPD57qggmwM4WsF8AmKIvRscx9dnA2jZsQwiM9SR5TlQVyIjR//qZy6S3QmL\nUIuKKjwsCUE6na579+6vv/76K6+8Yv/uCIIgCMK10EVkLebvn30P4A9vdFHe7MaX/wfgd6/+\nP2ccUy3i8tEMAL3HKI2Q8L0b7slfzv4M4M+DnOcr0UpIvbT/LoC+k9sz7wbDOd4NPgU53pun\nGefstqLZZMwyWRVg2qaqYZNja7MBjFrc2p7DA7BqVMmTpx4AVp2QdX/0mfTsqYSsUwlZQyMd\ne0aoZbHHhGshB4c26PV6vV4PQKfT7d27NyBAA22YIAiCIGzwBvO9GwxJ78a2GYUAZqr2n3+Q\ndBcyfStETWHblYYA/v6Zq4/DMZiLRbWpFyWcyYZJRQAWHHC2DMFn9ZhiAEuPalDbMSGu5eZp\nRoUNtPJubA0vBDBrp5ZjRCrLU/iMXCihtrB2rXErpROaWD2tct24wAhzbsc9AANntAUNURLK\nuEjgSE1NTU5O1uv1Op1u9OjR/fr18/OzQuK0Cg0EDothIUajMTs7W6/XR0VFTZ06lVpXCIIg\nCIJwTyx6Nxjk3bCBxLn5QJOwTWrDHV3LFxd/AvBav+fEXxJ4NxSaQa6l3YZiPaqT6TtZeM/s\niK4ci5MXct4NhemVFSNLAKwwG0/0O+8B0IW3FWxmv3eDseyYrBTIRk6YbOH3zCP1r2lVvywf\ngXeDuVQAL2tfhyDgohGVmJiYuLg49jGzPoSFhe3atctBu3OGg8PPz8/Pz2/evHkAoqKirl69\n2rNnTyfslyAIgiBsQ713g+GG3o1L++8A6DuZAvBswVoLjzuwe24+qtv1idqBM70bm0KNAOYm\nCQUITbwbTkZb7waAC3vuAgBk4zY45vYpA7DpQx+5DZTbtZS9GwyBDMS8GwzybhAKOH9E5f+z\nd5+BUZzn2oDvVaGZDu5GyD0nyTk5SWzHxpgmOsim915MMTZg00wxvRlsijHF9CbRi0URQgJR\njBOX5CQ5X3JsA2pgDAh10ZG+H+9qNDszOzu7O9vv65fYnd0drRbQ+8z9Ps+PP/44Z86c2NjY\nFStWREVFZWZmjho1avXq1WPHjn3hhRc88YpebTIaGxs7bty4kydPssBBRET+YEG/AgCTtljX\nD1M6FgOYu9/upmtuRaGA893pHwC81MhQMsWjAiW7IWhmNzTpTAbxVXbj/da3AHyaaHeBLfFE\nm2SXu2bmXLVbO5ixyyZSIbIbonVu2fYrxw4svwKgw3vW4sK83oUAJjtZu7lxpYL0tXj3Vo/J\nBVCl2oPignDYGQ27dlxOhYoYuljjr4CzY87NSqlQaPJ+geOvf/0rgNmzZ0dFRQGIioqaPHly\nQkLCDz/8EAwFDvE9zJkzZ/bs2d58XSIiCk2eSF/bM/mtmwDmHawyru0tAIuPOF5aeBSzG+4I\nrOyGIGU3jDTyNEvSlkwALft547WccjbhIrw7X9ab/9p4jjq7YSL1AGDlq6915dXn9CoEMDXO\n9YzJqtG5sFOYEMr2zjyF8h0iGm4WhYkvRHZjRtciADN269Vfdsy/+pCp/9hsm3kdQJ/prk9m\noeDm/S0qly9fBvD44+WppSeeeALAjz/+6KFX5JhYIiIKXSK7IbVk08luCBtWPAag3dva97bs\ney11D4BHARxecxmqGX4UEP72zb8B/P6V//D1iTh2dP0lAG0Ga0SKUuIzxGLbqeyGeo6y/0jd\nkw6gSZdoH59HIDCS3fC5FaNyAYxa4XRPConIbogChxFSdkNQZDe2z7wOoPf0h0/vTwPQqKN1\nYvTUjsXP/0bvaYcvtX4LnwzWaJ56fGtmi75RmtkNoTgvogdDGeQtljBvv+K4ceMAyMeMiCjH\nuHHjRAsL07HAQUREQcvh1dRDay4DUHRrG9zgDoD15ypKt0zrVAxg9j698se8g1VS9wBl2Y3D\na1w5YWFS7E0ACxKquP4U5LZT+9Iad3ra12ehJ3FDlsWC0lJl4Lh57yiRJvACv5046KHshtTa\nRrzD57+phrLsjPHshjSr1RNn6IekjjZGBgDP61MIYPI2J+IY7mQ3hBHLam2eln3/viUiQnlx\nO3VPevPeSN7+8Jyy8rfODpEP1tskUGbsrnp8a6b+Sztb2vh4QAGACZvstmVhdoP0ycN9KXHO\n/U8R08vmXzlnH+41Xi1wiCDK1KlTvfmiRERE0N3nLFqyGUmY7/2H9v+bYozC/Tvl9zK7Ebh+\n/8p/nNqX5uuzMEozvuHyRglnsxst+npqc8rC/gUAJm4uX8j5Z3bj+VcLT+wsbNY9sHemqHlh\np4M72Q2DkrdnAGje2+hPp/f0hyHLbkjmOAr3Adg4ORtAxvlKkHUM0fkLEjf7WnFBBICKlR8A\n6DeLhQnyhpQdsqqEk2kOm8c6/3Cv8WqBY+vWrQBeeeUVb74oERGRPe21yhDy7IYwe99D8/sW\nzu9b+OHW8kuFQxreAbDurPJg94P0zG74nDq78e3pHwC87AetOiVss+J9ivd82JLaJ3Ya3R8h\ne5JQyW4I9jrarHwvF8DI5eVljpT4jD+1Q0zP+jvmX4Xz6QZ39J+tnS65Xehf01h1shtuWj4y\nDx4YPUP+xm9jdybyRoEjPz8/PT19z549YkJMbGysF16UiIhIzmGP+pie9Y9tyjq2KUvnIrYY\nE9h26FOn9qX1fR9bP7VO5pOPURAFDgpof0n5CcCfYp739YnYdWxzFoCwsFJ4MkbhQ/LshsSF\nnqYL+hUCmLTFiV0MTrUL9VB2w7ctS1PiMx5/ARf/Wm3t+JyhiwJp9o3cjnnXgMo9Jj/i+FAA\nwJkvLwJ4402bzU15v1QAsGRoPoAKlUoAvPOZdvBk4Dy9DTjLR+QBeG9Vefmg17RHoBrsQuRp\n3m8yGhsbm5CQ4M1XNKHAYTFcCIqNjV23bp37r0hERGSixI1ZAFoP1LseLrIbR9ba9JCTxzdE\nu8cfvqkG1BqzxoOTCMzlt1Mw/I1fZTcAhEeUACgtscDXi+HQFDrvdtzsayhbjSvM71MIICcn\nAsAi5+dGybMbgvSu+qrp5g9navxw5saQRXWMHLx6bA7sDz/es+gXAF3GPya/cfO0bNimRZwq\nbax5PwfAsE9tXvHQ6ssA2g93d1MksxshwvsJjsaNGyckJFy7dk3qM3rt2jUAixcv9tAreiPB\nERsb27hx49dff/3VV1/1wssRERG5Rspu2Fv2tx361NxehX87WTglTrmFIaJiiRfOkLzDn7Mb\nVhagLLshChxzehYBmBqvN5PSc0S3OUULOtO5UIlzKrshiGW2/l6zCe1vAvj4kKd2kzlbQDG3\nyPX9sVoo2w2x99NfTHlOc60ekwvZ+BJNDz99y6nnfOPNZy5+k624UV5qEX1hXCOyG5unZd8u\nClszNmeYnbIIkadZLN5OcLzwwgsArly5IhU4rly5AuDJJz3VqsyEAkdpqbffJiIiItdoLgPs\nZTdcGHZgL7thJCTiNdIkCGitGJnp8FvJ2zNh2wNfEJ/nr78s8sE5Ba87xY6bL4h/Tx7csyAo\n/so4u53nlXY58Mswy+yeRQAavGny0yq2TdnLbpTtRnlMfVf/2XXXjM1R3Lh2XA6Ah+vd6TDa\nQZpDkd0Q3M9uUEjx/pjYF198EcC0adNWrFgRFRWVmZk5bdo0AH/4wx889IocE0tERCFBrOoj\njcWoxVpFFDiEPYt/AdBl3GNT7IwkbNEn4Jc3oez7c/8H4I8NfuXrE9Fzam8dAM17A0DzsqzE\nNyd/BPBK0xd8ld0QPJ3d8DfPvXjb16dgw9wqg7yT5b+/czxAxAsUGzH0sxvCv7+qMWqF3W0X\nGz68AWDQfJvdKP3n2PTRiJtzDUCvqeXbcxYNLAAwfqPrnT5/3SQPAKCX4Pj83Vyomn1smHQD\nwKAFhrbPENnl9S0qL7zwwvDhw1evXi3vxDF16lSR7PAEFjiIiCiEOLUMMJ7dOLruEoA2Q2yO\nn9apGMDsfQ/BP7IbUjRDf/pGy35Rk9+6mbr35ryD5fH7Y5uy4PwMUfIEkRqwWNCsR/mHeeXo\n3JHLPD50E6HU7yMsvBTAF+NyALy9WHs5au99MJ7YOr41E37TJtbZ7TzmfgxO7k4H0LRrtPtP\nNc1OsU+Mf1bPSNJxqzB8/cQbgxc6UVaQdxJVe+OtZxS3DLXz6ZJbNjwPCK9W84Hx0yDS5P0t\nKgAWLFjQuHHjuLi4hISE2NjYXr169ejRw3MvxwIHERGFBPmq3oVFWpdx1ryxvW0C/zxb/Z9n\nC4zP8NOsibhpWudiALP3On3FNTkuA7JQQAjy8+yGMHvfQx91Llbc+ErTF1aOzhVfn96fBqBR\nRyfWb/4vaXMWbAcVmcL4PwLP/rEwJb5QceTbn9TeOv361unX+8582NwT8zdT7WTWvMz0jRiK\n7Ibkk8H5AD5YXwNAr6mPrJ94Q36vO9kN4975rNbyEXnLR+RJtZKIyFIwvkFm8P4WFQA1atTo\n0aOHR4sacixwEBEROaa/ymoz5Kl/nlX2nxPZDTWxWcYLOdHFgwsAjFtv/Y1cv0fA/TthiRuz\nxDVneXZDYHbD58THZtbeel8dtjZ9TInPEKtu72Q3BP/PbszrUwhg8jYnluUr38uFaqiHddNZ\nH6TEF8pvN1IZMZ7Y8pPshlm2zbwOoM90m6LP56NyAbyzQuNT+sUHOQDe/qQ2TMpu6DOe3fj6\n2PnXWj0HwKnshr3PhhieUrHKgx4fPrpiVB4Aae/Mp0PyAby/zqZ506Yp2dVroyCHyzQyn/fH\nxHof/+YQEVHI0V+c6C9g7t8pL0zI+3Eaz24I5mY3BHl2I3l7JmB07F/zXvVFqJ7807FNWZYw\nVKl1T/zx78dqA3jxNWVNLciyG2L7Rsv+NiUAs3ZLSX/BU+IzgOoREaVfvJ/ztqqJY9KWLCCs\nZT+NlwuU7Ib+NNMA4v72qNMHLwJopNokIlexcukfY7Ph0n4WAD//XxXjuZ7dH18FKtm7V77V\nRdGPg8h1Xu/B4X0scBARETkmZTf+drImgNaD7B4psh5iwpjmMky+Wcaj01X+u1le4vaHx7W9\ntfiI496q+ucgNuaERZQCaNbd36/h+7mTu9IBNO0W7drDX2/37N+P5cL+Sm/bjOsA+swIjOW3\n6YxkN8ROn1llBcEXXyu48K3RxIc/Z1h83iFFkd0QNLMbgshuSJYOy6/z2F0AfWc+vHhQAYBx\nGzyyJUTxRsXPuQagZ1kz0e8T6o5aUVMUOJaPzAPwaL07ALrLRsZqiulZf+v06+rb+88u712q\n6Hta74XbXScon/bu7fJdBHN6FcJvNgpREGCCg4iIKIQcXnO5tBRAhMH93i37RZ3al3ZqX5q4\nyndqbxoAL/zfOrVjMYA5+8vzGkfXXwLQZnB5KkR0CUncfsupZx7f9haARQYKIsEkJS4DsgMx\n3NIAACAASURBVDkg36b+CODlJp5q8O4CUSlLic8Q21IUmymMGNvqFoAlxwLpJ6u5fcNDu6XU\n2Q1Bnt3wefnANf6Z3dDcFqTP/XdeP7sh+iKNWmH9V1T8q/735Dz1kbsWXgXQbaJGvcOpXI+6\ntCFIdZ9FAwtC4oI7eZElBD5QLHAQERE54cOt1aC7z9+pbojq3ETqnnQAt/Ij5NUKlxnJbtgz\nKfYmgAUJVaDVVJVc5nJ2wyCd7IbUtiPEzXK+Ea+a6B8hKJIIvuInP9xNk7MBDJhX1+GRCmPW\n1Iifew3AiR0Zf2hpMyfIXIo3SmQ3RIFD4b2V2hv9fvpHlbm9C6dst/4XsG78DQBDFuk17LBX\nFlk2PA/A6NXaL1SxYql3OptSiPDDAseJEydiYmJKS02LlrDAQUREZNVu2JOHv7js8LDUPekN\nOuDcgTqw3aHduPPT8kDHp0PzAby/toa8VYcpRHZjbu9CAOI3bFOqIXA1uyH6X+pPn/VnMbbj\nY+TZDcXoyq+OXADwettnTXx1dR5H0/aZ14EqvWVbAOwN9NG05FhlEUAgBbHWTd6WCaB5H5s3\nc3jjOwBWn6ooP5JWjc4FMMK91rYupJD0HVl7CUDboU78S7hj3lUAPSZbKw7G+yJ1m/io+OfX\n0xyWNlaPyQUwfCk7dJBhfrZFJTMzMyYmxtznZIGDiIjIKiU+o1I16zJGsbJVc2pMg2R6l2IA\nM/eUr2al38vLtpkoX1HcLq66tB6k/BU8ccOl1oOeStyQ9fWR2opndpPIbmg6c/AigDe08t4p\nOzIAxHjs0iu5zK/W56KBqIkzRIyUe45vy4Q0HsUl8l0qF36oBGDhIbt/TYLSgn6FQESNWvfF\nHzXHS7mQ3ZD0nPKI5u1udq7Rt3Z8DoChi2oDmNmtCMD0XVXVh8mrCQ8/dg/AV4cvvN7uWTjK\nbuxZ/AuAbhMf07y3avUHmrdvmpINYMBc199MIjW/SnBkZmaOGjXK9KdlgYOIiMgJiwcVALX/\nq3F+0uYs+a/1J3amA2jW/WmUJRreX2u9Vz+74fJQ+inbqyVu0MhUm0LanyIqMpGRpVPjq9pL\naqhv0U86pO5NA9Cks9+N/Dh9IA1Aow7lJ6aocBnPbpzanwagsYGxJiK7obM+T96eAaD3dGV5\ngvuGTNRcq+ohZTfMsuTtfABjv6jh8Ej/NKNrUaXKgNvxDbktH10H0G+Wuz1xNbMbexb9AqDL\neO3KgpTd8Bzx3VWxk8MQjUgqas1R2TbjekQk7t+zLkbjZl8D0GuasvrD7AY5y38KHGJnytSp\nUxMSEsx9ZhY4iIiIrGwnR1pu5kUkrLpcpeZ9Ey996yQs7G0z0by9bNu29a7Wg+rpDHZRmxR7\ns1W/a7ATUZnfp7BGDeTnh8tvTInP+Om7GgBaD8LtQpvfH6Z1LkbZkNr/Sa4J4NVO2U6cTbBw\ncx5n9cfvfHPip1eaPS/++NXhCwDE9eHg4052Q5EaEKmK5r0d/yXVz24Y6SEqvzfosxuan+fb\ntywzdpenG5xqOeSQGEGi2afTo51rRHZDkGc35MkO2FYTRNeVTVNKfjqX7TBk0WWcdoVF0Klx\nM75BpvOfKSoxMTHx8fE9evSYM2eOuc/MAgcREZETysYWKi/JNeseLX2t043iyLpLANrKNnsb\nb5+hmQY3bny7mwAWHa6yeFBBXQOXS8V1HlGRUbRv0FkofrBOXJ22XqMWPVMtllIAjcsiGyK7\n4XATkPc16vD0sEZ3tn96Z81ppy/dn9iZLv9UGMluyDXvHfXNiZ8076pkJ8EeNI5tyoLHxqNI\nnC0YKRa3pgjc7IYgL22Yxf3shg572Q3TrR2XA2DoYuWnpd+sh+f3LZzft1B0pxaksU2iEcmG\nSTfE7QkrfwYQO/KJ+LnXCnIi5c+jyG4o5toSOcFvEhwZGRlRUR7JIbLAQUREpORUZEM9otUh\nsdejYrUH0FrhSztBdJ7ZXst948TrzupeBOCjnVUnv3UTwLyDVQB8WNZe5LOReQB+1aAAsLTo\nE1VyPxMAoHzp2cZmUpzen9bIzrL/7KGLABq21xviKOfmhvzU3ekAzh2sA1d7qWh64dWCZt2j\nT+zIAPQGQHyddB7Aay2fU9wuZTcExVLcXr7g2OYsAK1MvZDu5xQ1PrMCVj7pUWJv64FxUzoU\nA5h7wLTmOxJFdkP/L53YDJLzSwU4P1Nm+6xrAHp/9IhTM1ZNIYa2yBt/LOxfAGDi5uowUN5y\nJ2Gxf+mVjmMeBzBoQZ0vP//5y89/FjXlhf0LohyNqM67HungCCI7Xm5c/vH67swPTj32pTde\nlP/R2YcreKi6ARY4iIiIvElkN0QJQ2FMy1sAlibZnWPiZhp80WFrol6EUIY1ugPgycedeAax\nrUAxb+LjgQXh4aUoz27YaNIlWnxxen+a4i7vZDdGt7gNYNnx8m3uy4bnATV/F5Onefya0xVP\n7ko/ubv8DMW6LizcJoSyeFAByuM8gG2Ex33yZg0NY59BWYHDyDaKgOPp7Ibg7GYfc7Mb/s/f\nPlrz+xaibCy3/1NnNwBs/DAbwP37lcLCSuUzZWN61d+/9Ir6+NiRTwD4118KMn+sJCosRKb7\n/qvyqoSzLcDkj3Xh4V7DAgcREZFzFMEK6YuPBxQAmLCpuuhJcetWWLOON6DV/U5nD0vqnnQg\nvPWgejO6FQE1Z2g18xclhvt3w6ShKk6F/MXYWuBRAH9qkyseJbIbCu+uFGGNmrBz/XbJ0Hwp\n8CpiEU1kZQsxUSWiQknJfQtgsXft98SODCBcJ/IgObU3DUDjzk8bzG6c2pcGaJdwmnSNbtLV\n+vX7rW8B+DTR8YhcseNGqtpIxOJQXICVfyMfvnmz45jLsE1nqLMb9nx97PxrrawHx/Ssf2pv\n2v27yt8ojWc3PnsnD8C7n7ub/SETuZPdEDyR3dCk/5fOnc0gvT8qfxPE9o2cyxUBJ76vGV2L\nYGAHzeoxuc++VAjZJjv10BbvVBZEdkPy5jtP6J+AovxkYpNXCjkWf+nB4TkscBAREfmFpUmV\nxfpZbU6vQgBT46oBmBh7s0V3E16uy9CrJQ+Um3GNFEqa94k6uSv95K50seCJjCgdu7YGgNTd\nueKA+X0KIdvn4ludRlzZseyJEY3vrCqbiGFvd88nQ/IBvNQ2JywC/3e2BoCmXQHbdZ30A5Jn\nN1L3pIdH4sE9M3c2j/2ixtfHzqtvj6hQ0tj/ps84xd4OHdP5WyTBz/nbGyVlN9ZPvAFg8EK9\nOaxe4MLHaeB8uxtY7D3broVXAXSb6PHxLhSyvDxFxaJ6vdJSj1dYWOAgIiJyTpvBT6XEZ6TE\nZ0i/nopMx4RN1jyFrCeFE405YBsNkLIbS4flV62GosLyS/fHd9aN6ZYtxTcgK0mIFEl4hN09\nI9AaW6tIg4uIh3TY8W2ZAFr0iZY/xGJB6u70/4qxxPSoP7tHEYBpO2wOAPDdkVoAJm6xuSAp\nNdiTPRdgrK2GwbW9vZyF3Kl9aQAad3r6xM709gPRrHv0J0PuSvcO+1QjcJ66Nx0WhEWUnD54\nsdFbNu1CzuypC2DaDpsLyGcOXmw7uDy7IQoWUiLD4cmrj3SztBEo2Q0x/Fj+8ZZTfDhdw9qH\nKc4cvAjgjbeMts6RnNiZAaBZd4333+ZfBsMMdj8dvrQW4Fz2wV73UNMdXSdmfms31+Bnlczi\nP1NUPIcFDiIiIh9QNxAVjTmSdtWF1naJqXHVxEMWJjwFYGL7uhaLqDu4tdITkQ2pb6golJTt\nYbGrabdozbCJKIV8uM3mlBQ9O9TEOkcUOMzVuNPTABp3wsld6Sd3ITyy1F6XUwAfrKvR/093\n//HPJweO/1l++5Zp2QD6zda4GCvCCE26PHdmT5EpJ/zJ4Pw/trG5xcSleOLGLACtB/q4HakX\nshuCr9aEnq6e7Jh3DUCPyV6aoCF1APXOy6mZmN2Y1qkYwOx9jje/DGl4B8C6sxUB1Kh7b9eC\nq90mmfkDtffxkLIbm6Zkg2NiyRO8m+DwQl5DjQUOIiIipyl+PXU4QkW0lFPsu3ZIlDx+9Sfx\nJ0MDJidsqi4iEg/uWww+BKpOforL45EVS+w9sOSeBarkguSurGFE8vYMAGERsIRrX7l1eSSK\nwomdGYBF/hIfDyh4uS0AlNy3pO5OF11CRO0DWs1BRfRjQb9CAJO2lL8zTTprn+HJXekNO1nP\n/+/f/wvA7/74a6iubDvMbgjfH631wXrlD+741kyUNXn9c9J5AK96q0zgffayG4Kb2Q0hsK6H\n+23exIXshtCse/1PBuf/LSlf/VHXcWTtJWi1NDKFvX+ihy6uvWvBVfH1ugk3AAz52PWCi84e\nwDZDPPJ9ESl4eYuKT7DAQUREZAJnFyFSTSRpcxaAlv3ric6jrQc5eIiY57rwUBVAb6X34J61\nuPBR52IAs+xMcjU+wGLRwAIA4zdWR1na4v6d8vqF6Cfaok/5t79wQEGFSrh7O6x5nyjx5pSW\n1UmSt2c27+2p+XAAKlR5cDbhohg+Inx7pPaETdVFG9QTO9PDI0shq3FI39Hmv0QrnurEzoyn\nfltekUndmwagSdlWERFGsJc9kXbBiD8a+YR8sL7GsY1ZXx3ORtnUD3G8KHC4yefZDReI9/BO\nYQSMTRFaOiwfwJg1ymWztLA0Mptj98dXAXSd4GIfBE9XIryW3RB8mN2QHFp9GUBYuLvPYyS7\nIYjshtBtkhOfhI2TswEMnGdNXmydfh2As+Nvlw7LByLVH2P4WYcjCkTcokJEREQmUF8YnNDu\nJoCPD1cBcDSu7tG4W0uOKbeliJLHkNfvQFX4SNqSKa7DnNxTVzEAZdPSxwH0GnbNtVOdGHsT\nwMIE63Me25QFhLcaUO/bIwWKI0WgQFNKXIbY6y4PdzTvXR9A8nbn1uqi9+etm2Gix6o9J3en\nA2jaNbpZ9/pnEy7K75qwydoEpEnX6BM70w2+rshunNiZ4/BIefZEZDd0iCSLeCuMa9E3KnFD\nVuKGrNaD6nkzu+Enu1oEN4MMERVLUuIzAD8a/vrZyDyUzyqyS/0tT+lYDGDufi/NT/EEp7Ib\nwg/fVBP9jN2xemwOgOFLagPYv+QKgI5jH4exeJ072Q1BUVBeMSoPQOUqD8IjS53ajSL/74PI\nOUxwEBERkRHqRYjYYKIzEVZo2b/e0bhb6tuPbc4C0Kp/vXVfVZTfLsoZSVuUx09ofxPAx4es\nv/JKCZFZex9K2pKZtOWGm9l+kd0Q1NtJYlRDXl9qmSv1C5TenLJ+pVEoW6/evxNmMEVyam+a\nkS6bC/oV/qmdNVpy+kAagEYdrI9S717R+Y4ExcFNZCcgTQW2dyaNOz2dtCUzaUumeOfFmyAK\nHDpayaoJUu5g7bgc4KF6vy7Wf6wOv93moE+eshHfgj71Re+zhy4CaDXgGfFw/eyG4HJ2Q4eo\n63k0uORpPvwItR/+pPGDE1ZeBhA70omHmGX9xBuARd40pO/Mh9dPvLF+4g11J5FNU7LtLcQ0\nsxuCyG6IAgeRC/wwwWF6nw4WOIiIiJwj8tIGf+ee0a0oPBwApsXbNKqQX3xTZzcUFBfSj23K\nAiwtB9RL2pzVpNMNwOY6Xu8RYse4smows3vR9J0azTLk/R0ALEyokrgxK3HjDfFyEZXsNuB4\np+ltAJ+frCTdMrd3IYAp26uJ0kZyXAaA5qqxCGKbwCttrX+UwheKw1L3pFvCSl9qi8adnj61\nN83eaQjyh//lcJ2X2+SkxGdElr2vc3sVAnjtLeWjFFtOvEA/u6F/Pupimb0dGWaxl93w/vsG\nO0vrEzsyADRT1dcMPlzu0yH5AN63M3vIExxmN+wJ6OyGPZ7+MAsiuyGI7IZZ3m996ze/c+4h\nVWvcd62NKLMb5DJLmONjAh0LHERERB4hlqN/TjQ0X+PYxizYXr1v1b8eykobRkjZDfWitGW/\nqJndy09DyoYYfGbBlN0KIrvx3bFCyBacosChz/iEVLG1JCU+B8C9Wza/ypU+sBlCOadXYcPO\nek+leclarKhLSzFhk90Fs0heVH347kN1cLsgQpoorH5CjaG5MlK2xf0plR698K4z9VOQGs2Y\n9YrG0wQN27vYCNNcAZ3dEAIl/uOT7Ibw//5eRT0Ay94UGDcnpIiar5ujoykUWfwuwWE6i09m\nt/gti4VvCBEROU3aEe2wz7+9S9/qAodgr6yQuDErLAxwftE4rs2tNn2vwf6i2h4XChxi3GzL\nflGKkIhHyecU6CyD5/UpBFBSgtc73oBWfkT+8NS9aVUfvQvgpYYvSgUOnfWeVOAAcLsgQjoH\nMWOl6ErF19s/a30J3QJHoPBagUN6npT4jErV76OsFauwcnQugJHLarn5KgTPbEgxPqXVc+Ln\nXgPQc4q1c6p80xyAbTOuA+gzw4meoOsn3oCps2zlDiy7AqDDaI2kCQscRPYwwUFEROR76tKG\n4H5/R/m405O70tsNRMl9a5MxqQBh5Hn8odPk1I7FAOYYzufLl2eiMAHb7QxT46qd3H1D8aht\nM68D6DP94Zie9b86fOGrwxeA8iSIkd0QUvLC3nrp1L400V0iOC6q6JQ2BDdLG2e+vAjgjTfL\nsxjiR4OA7S1CQSZuzjUAvaZ6b+IMSxtE9rDAQURE5K6PD1c5uv7S0fU5OtkNwbW2BfJUguBO\nucGUvIAYR9Kse7TeQaWut2uf3rUIwMzdGk1DoDsrMdJ+0xAAsODEzoxm3etPLnusZnZDoUnn\npxf2LwBQeCVdekjqnnQATbqUP3zztGwA/WfbTZ6LGSti9KkgFZskDnuXBqv//d//BfDb3/5W\n5xhRKBE/i5daK3+JVWQ3xFTgJgZ+vgFnw6QbAAYt8EhqQPBEzciH2Y0vP/8ZwJvvPCFlNwQp\nuyEYzG7II2kiuyEKHKbTzG4QkT4WOIiIiLxhRrciADN2aa/YBfcvR6tHt/yheS4AoBrszwpx\n2YkdGUYSDdLmlORtmQCa93F6r4pYUczZ7/omF3GeYjOFqHFIdynOKiWxBoA+0wHbHRAu0Fwv\nySeDuGnZ8DwAo1e72KvShz57Jw/Au58bOnN5dkMtUOIbiwYWwHYaEQUNKbuxc/5VAN0/dDCI\nx6MbW4hCHAscREREJpDGsjrF4KZ0g4NUnaXYnOJU89Fm3aOlTR/2/ONUDQAVHkqXxxwMspfd\nEBr1FNdLyxMc0jQNewUX6QBR45Cr9Uz5zMXZPYueiVaOvAEwcbNYl5avTtXflE52w56Iisq8\nSQhmNwT97Iac/GeREp8h9XAVPh2aD+D9tTUeqNIxHrXv0ysAOr3vjUvuHs1uBKU333lC/4Bd\nC64C6DbJ0IRgI+2EPhmSD+ADA0N5PhlcAOCD9SH6F5/IdCxwEBEReYN+dkM4tOkRACX3XW/J\nqZ4k6rDFhhja+vtmeXBygJzD7Ma4DdXFPg6JkeyGZmeQFn2j1EtZ15TqbmHRNLVTMYA5sjqU\nKJfcv2sBkBj3cOVKJXMPOBG/lzb4qPf4nNyVjrKsjdRH0N4EU49mN9Qbo+SWj8gD8N4qBydg\nL5RkMLsRTNzMbviq28i4trcALD7iYJQ1CVJ247cNCwAA2gUOzeyG9xt5EAUlFjiIiIi8Qb0+\nObT68p9aov3w8qGGS45VBnB8KwAcWXvJYUcP18iX0Ar24hufv5sL4J3PbHoc6C+ABXvZDcXw\nAknixizAEhZeemJHBiyl9np8qHeOOKy26Bzwxwa/kr6WshtSQ9Pvzv7Q4X0c+NToz+LbUz8C\neLnxC+KP3535AcBLb7xo8OGS9RNvPPeKsw8y6si6SwDaDjHhAza6xW0Ay45XMnLwtM7FAGbv\ndVAMMj4PRb3gf39tDXt3eZR3shvBxK8axOpnN75c8TOAN0c5iIG4YH7fQsDy4VaNdkJE5BoW\nOIiIiDxiSsdiAHMNj/yQtOgbJcbNekfZ79Y2v2EfXX8JQFh4aasB9WZ1LwIi6zxyz5SXS96e\n6fCYyEolpSXW/QXSCFLjCyGdCg6AmJ71Z3Uv+upA0Uc7HWdqJHP2PSQmy4rupBs+eRzAtm8r\nAGjZr/ywLdOygdr/0TxH/9nUhZuvjlwA8HrbZ+XT6s9/U11c6VVnN7xAv3Slk90QP+LmvaNg\nu3xN3ZsW0wspceUXqEV6yOXV3eap2QD6z3F6Z1Ag8lUhgNkNp6wanQtgxLJa8r8+a8flABi6\nuLb+Y5ndIDIFCxxERETeENOzvmKThTy7oeCh7IbgWqvRZ/67+Oj6YnmrEc0FcNLmLNgZCypS\nGxYLIMtuSI0/xAO9M4z2tdgcACd3ZcP23ZCHL+bsfyglLiMlLjum14sAZnQrqlHT6OYWKb6x\nYlQu8MioFY6TCGpSiH1ur0IAU+JMu8Y7uUMxUGueM3tqdBjMbkgcxjdgILshLv4DPpvKERzU\nk4DklrydD2DsF+YU1+JmXwPQa1r5Gt5Pshuadsy7CqDH5EfLtmKZn90QmN0gMh0LHERERB7R\nrFs2AM+twVyeSKLv2MYsAG0G1wNwfGvm8a2ZH+2MAnB0fZ4pz396X51ZBpa4KJ8IU74KMrIp\nBloVnA/fuglg/sEqOo/6c/JP4ZGOz+of//P/AGz79jea9/Yr6zNa1spUr+3C6f1p185XBlD3\nmVtAmBgh7GDybiAQ2Q019YxkN1d3IZLd8Ge7Fl4F0G2iod6cwWfbjOuwHZY0Qqs2J2U3ODyF\nyAtY4CAiIvISf75iKYxofKfDAO272gx+aumw/B++yR+zRu+KrmZ2QxCpjRZ9bG6Uun5IDxQN\nOO/fCQNMnoIhbV1R9/4QHtyzvNr8eemPJSXlJyD1iP3H/zixe0id3dBsoarp9IE0AI06PG0v\nu6ETlrFHBB/mHTDnc7hiVC60vkenuNaIQf94v2ru4GVO1T31xxuZld0Q5NkNn3P4Cekx2Vqy\ncdhGV+LNMTpEpIMFDiIiIo/Q+e3ZlAWYO9kNEdNoVbYfZFCDuwA2nKsA4MCmR1adqihulya5\niGW5vYkANs9sLGRhhHoijOJpk+MyADTv5fhtFNmNk7vKbzl94CKARh2ekW6RlzZmdC0C8Ppb\nGk/1X//9GwBr3s8BMOxTjU31ZR0oHJ9Vo47KRIMLjFdMPGdGtyLYzgk6uTsdQNOu0T46I/IS\ng9mN5SPzALy3MiBH5xxYfgVAh/c0Khfy7IYRzG4QeQELHERERAFAf7e8Uwcf25hVueZ9zbuk\n0gZURRAA+tkNsxjfo1Fa4lzEQ7P5iJhyUvRLRQBNukZPaH8TQJXKgKxRyF+//j8Af3jNOmzl\n9IE0I7UeTcYrEY06OCh/iOxGWe3JEHNDDW5mNwRP5Cw0n1MazWv6y3nZhg9vABg0X3upbPqe\ntaBk7qeuLMr0OIADy38uvBEJoO9M52ofclunX3fzGYhCGQscRERE3ubsr9dH1l6qUraWLOtM\noRGRcC09cWxjVvehNoUMQb5BQ39ZLjUKdeHVjTuxIwMGxsHKLehXAGDSFo1GGPLshtqM3Q4G\nrPxHo3x7BQipA4Xo0Vitxn0AQxZZl6Ope9NQtvfmdlEEbEfzyuePGGTvR3M24SKAhrF636ZZ\n5NmNyW/dBDDvYLQXXldw4YNhUEBsdXHqJD/qUgxg1h5vN2cN0OyGoJndUIiIwOoxucOXOlfy\n2zItG7LGPURkChY4iIiI/JqY2HozN6Lt0KdO7kqvWBV3isJ1jj/8xWUgst3byhEt77e+BeDT\nxMrqWoY7kuMywiPx4F6Yic9pkL1WGk556Y0X5X/8+JBGI9I/vPar707/8N3pH15q9CIcZStS\ndmQAiPHAettD/GGTi5v+mVrjn6l5o1frraKDILsh2MtuhJo9i38B0GXcY74+EYxaUWv1mFzx\ndYf33J23khKf8cSvrBWrL1f8DODNUZ6a4UIUlFjgICIi8neWMMjns2rGN1CWnjj8xWWnntxe\nvUP+ivak7kkXcQQphiBPc6idSzwPoEHr55w6Q6FZj/pJm7OSNmcZaaspci6TtjhdypnSoRjA\nXDsjVMWU3zk9iwC8GnsDuh1ANHs0qieJyKmzG2cPXQDQsP2zDs5bxXh249S+OgBa9nP2FbTN\n0x1V4zIxY1izpNWsR/1/phod8ZO6Ox1AE2PNQfw8uyE4dZLez26EglVjcgGMMBzfkEI3zG4Q\neQILHERERL6ncxVdKjQkbswCwls7yl+EhZdq3v5pYmX5H2d2KwLwattcuLSvZNHAAgAvt0NE\nhVKDnUHM8lHnYs1Bs//4278A/Nfvfy3dsnxEHoD3VtUUZzt+o97QVuGt934GsHjwowDGrS8/\n/qVGL4pliUPy7IZmM5Szhy7cyo2ErIeruQJiY4W59LMbLgjB9zDgeD+78fGAAgATNln/WXCn\nXKtD/qljdoPIBSxwEBER+aMRTe4AWJVa0eGRClJBRKyu794Mg+ENCCd2ZgBo1l17XSfffPFK\n7A0AjTsp8wiK7Mbp/WmQjQtxczHQsn+9jzoXK25c2L8AwMTNNsULEXL58du813tf/f7sVcC6\ni/7bUz8CeLnxC9+f/QHAHxu+CGPEire0FMlxGVPjxfvjoE+HKVzIbjhLxFWkfiVmre3FjJtv\njtSevE17zK1TFNkNl0/SYHaDyDiH2Q3FBNmYnvU3T83ePDW7/xwmOIjMxwIHERGR7zksQEzt\nWAzUnrNfO2E+ucNNAPMOOLE7YLq1MaTGKl2eO9DfciLM71MI4MNt1aDbA9UUmtkN4b9+/+uU\n+IyU/8uQ1r03sq2/5xjJbgjF1yNhm91wh2a2xcSaxaweRQA+2mHzQ/Ra7iAImnfYw+yGz/nh\nJBEpuyGc3fEIgAatjT3Ygn1LrnQa67hfKRG5iQUOIiIif6ST3Rjf7iaARYcdlDPE6jppc5bx\nFxXZDVHgEF9UfAh3iq09TUV248vPfwbw5jsOhpgKtwtc/E3D+ESYl9uI9n4aJYmZu6sCNhmN\nlxu/IL6QZzcWDSoAMH5DdQBN7bSi1F/xjm5xG8Cy45Ucnq0LNk7OBjBwnmcv9kqzv0JFWAAA\nIABJREFUZsR3mhKXASBG1mHk8BeXALR7+ylIHyrdKb2iO0nzXoZefWrHYgD26neCPLUh/3Ho\npDm408T/rR2XA2Do4to+PIdtM64D6DPDtGLKwc9+BvDWu9YNJp3ef3zfkivyA5jdIPIcFjiI\niIgCgFj7iWvmgPKXY3l2I2lrJoB/na0BYMyaGjotOce3vQVg0ZHKAKZ3KQYwc89DUOUOWvWv\np19u+FC2B0FkN95rfhvA8uRKRhqCukk+H1S+lN214CqAbpMe9fQJmM7hhFdp3a7IbujwxFI/\nPLKUtQPykCd+ddPXp1BOM06iCHQo/Pvbav/uVzhpi/XfRpHdmNapGMDsfWz1SuRBLHAQERF5\nz/y+hQA+3Oq4K8GnQ/MBvL9WYxKH12jusKhQ5YEXXtqFvqcuE9kNd8izG2cTLv50rgaAgVrj\nPBM3ZgFQdIrVGRECZ7Ibf0n5CcCfYp43eLwOKbshVUZEdkNo2b+ew5arTpVUpOyGzqPsPZXO\nS7D+oiBGLKlnSPuQb7MbgonZDeGtd5/4d79CAHN7F07Z7vhf+y8+yAHw9ifKt+KTwfkAPljv\ny/8FiAIOCxxERER+JyU+A9AYDCH6HRzfab22qV4tH11/CQhrM/ipln3LHyXaPaoHmorshjBT\nd35kqwH1pBGtDse4AFiebOZmjXNHLwBo0Ma51hVuZje+OfETgFeaPQ83ZrW6xuGEV8W6/S/J\nPwH4U3O9uob7S33FxWfFE0qFic/eyQPw7ucmjzUB95uEGI/+oMWGEeMdMVxoBTJpS7W5vQsV\nNzK7QeQFLHAQERF5j5HshvC7Znn2fsWXd9/4LqnWd0lFU+Orwjq6tfpvGxbAWukon6jijrJ9\nMTYdF5K3ZwJo3ttug8mVo3MBjFymPV/AeIsNE+nPiHGKukWFpGHsMw1j7T5QszxkL7sB4M9J\n5wG82tLQ9BlnsxtGSgbye1v2v3rmIN54q7z+cnTDJQBtBml8zFxboLJ+4VF+ld0IYgkrLwO4\nd89o8kKd3RCY3SByAQscREREfkdzmbdseB6A0avLr43/+E216jXvF+TZ/G+uLmrIsxvHt2YC\naNHXZkUt3ah5r+Cwm8a83oUAJhvIYzvrVp4Jv66kxGdYwgA7ZQ51vUZkNwRFduPEzgxLOO7f\nsSRtyfSHGSKK7IZrSYclb+cDGPuF3QXV7H0PnTlo9+HSy5mb3ZCHj1j7ILN4aJqJoh/wjF1u\nTZJePLgA5k10IgodLHAQEREFpA/fvPn4kwAg4huwHYZqSnZD9IbQXMPrZDcEe9kNQcpuyEfS\n2qO5snVhHm1pCWJ61hcFDmF2zyIA0+KdWIec2JkOwMEEEfMYzG6o2duXJOdsyUCe3RA0sxte\nIML/oruB8b42FFJ8uKcpdiSTMkQ+wwIHERGR31k2PB/A6NU2l9Pl2Q3hyuUK8790MCxWbsWo\nXKDaqBU2pYdjG7MAS6uB9QCUlmo/cOBrdwFs/LqCzpN7IruhcGxzFoBWZVmS6o/fPXf0gqI3\nx4HlVwB0eE95hTamZ/3UPempe9JLHmjUJjTrNaf2pQFo3EljIK7mPhexl0fUg0QdpJmdobNy\nJ3ZkwHYWjJvEik4UOOxRNzXUyW74kH6Bhshz9iz6BUCX8Y8Zf4jLs5z3fvILgM4f2LyWPLux\ndnwOgKGLfN+Qlcj/scBBREQUkJwqbcgt7F8AYOJmjeSzIqyh0xtCk+YOl9k9igBMszPQtEmX\n6JT4jJT4jJie9T8Zkg/gg3WGVtoP7oVZX1RrBIm81qDDqeyGYKRmIUmJy9Ds0yE5tS8tvAIe\n3A1z9jQcCrLSgOJqvHwyBbMbpClA9zSxlkHkJhY4iIiI/I4iuyE3MfYmgIUJrlQ3RHZDFDgk\nrWzbXk7vUgzVUBWR3UjZkQEgRpU1MKVj6PFtmYD2d31yVzqApt2iS+5bwxcL+hUC1f8TaDfs\nSVHgkIjsRlljVHzUuRjArL0PwdFGGMl3Z34A8NIbL2pmN8pPWFXNkddTmnWPTtHNUMg5jG8k\nbc6CgTYoxknZDdee2WD9iAKOutFPyHIqu+EmRXZDjfUOIuNY4CAiIvIjq8bkAhixVK+BhZs0\nsxv2iA0UD+5bAIQ5+q1BszupveyGJKZnfVGkMJjdGNH4DlChfv277YY9CTsxE7H2PrkrHXB6\nvqNa8vbMKrXvQWtUrVgQnvn6IQB7/h4pv0ud3ZAqNdIt+jUUTctH5AF4b5V1CXr64EUAjVTd\nMdRca0lw5uBFaHXf8LIAvRpPIWvle7kARi63+y/59lnXAPT+6BHxR7EtsWqN+0MX1fHKCRIF\nLRY4iIiIAolmdkO+cpYnC4yvaZO2ZJbcDwMwc49228jz31WDncqLTnbDYIMJ/b0wUkWgzeCn\nlg7LF19P2qLcmKDZMqNp9+vygoLC3F6FABp0uCF/lfBIjU4ktwvCdc7QoVN70wC43JrUxOyG\nXEpcRnik9rBbHR8PKABqTthk/nAHzY+reiKvD5tHBj1mN4go0LHAQURE5Ec8mt0wTuw6AQCE\n2es86iud374KANCriZTNEIlW3D4p9iaABbobfH7/6q8UtzTvHSWahqqnyf769YLIKg9+1yL3\n32dqArU0j5GoSy2aM2ttD0iHrPFHyo6M3zS22SVkJLshiIrAt6d+BPBy4xfsHaYoH/g8u0EU\niHSyG4KU3QCwa+HVx59Gt4mPevikiEICCxxEREQBT75ylu8TsXeVO2HVZQCxI8pnGTrsp+Ba\n5cXl4SBS9EPR6mLMmhrJ2/M1H9K409N/Pn7+z8fPv9rC6GjVKXEiBuK4S6UoMYjihdCib5Q4\nNyMad9bbipK0JbNi1QdwaceKO5zNbghuZjd08hfaN6pOktkN8k8r3skDMOpzpmCIfIkFDiIi\nIsKRtZcAtB1q3Z/y07fVADz3UiGA1gOd2x+RuDHLhUdJpnUqBtC0m90DNMMRAI5vzaxW1qqv\nea/60nAW+TE62Y3EDZcAtB6kvUNH86XltaSIChfPJuQ2jH1GHKOT45AreaB/v83QlsSNWUCY\n5hubuCELQOtB1rukbR3qn4XIbiiqDPKdRM6WD/Q3jOjc6/JOE25RIdesHZcDYOhi7YadO+Ze\nA9BjyiOK21eMykVZh2bXrJ9wA8Dgj+0212B2g8hELHAQEREFP8V6W57d8E9S9EOzcamcfLlb\n+EsFh8drSt6eCbg+qzVpS2aVWsqTMaTUAuBfX9UYs6bGZyPz/pGc95/N8lA28CV1bxqAJnbS\nH6cPpAFo1MF6b7VH7351+ELZfiLtX/Bm9yyC7XDc1D3pABw2BzGxpqB4EtEGpSxK44TP3skD\n8C6vlpOTznx58Zsv6wD475g8uPqp3jwtG0D/2XXlN+pnNybG3pQ6KPHTS+Q5LHAQEREFCfUq\n9OiGSwDayFIJYkGrHpgqZTcE8Zv6sY2FF/+n6qrRucWF4QDGbTC0N8Gp7IbGsNX+osWGTesH\nh70qpCeRwgsO1y1idIvU3zSiYol+4KKsr4fyaUXx6GZuRMt+UeJHAKB576iTu9JP7krXaXF6\nYkdGWDhKHljGrFGOj1kxKld9xVjnjQ0LR3F2hYfq3hV/jOlV/+j6S0fXX2oz2OYhUc/dBgBU\nlb8592+HAWhe9j44OzZF/33WuVfc9eeEQoMvpHjgv87mOftACnEiu3HmS+1Pjjq7IbiT3RAG\nf1xHjPc2bvnIPADvrWQFhMhpLHAQERH5KfXi31kRkSXiC4d7JRy+3H83y0veltdcNu7E3lX9\n1WNzAAxfop0D17dkaD5QZ+xaQ/NiBVNiBRaLTSfV0/vTADTqWB6dOL41E7BYwkuhNRomLNz6\nDmueTNKWTNh2OZHnJqQfzW8a5QNo0iVaROJhP7shSNkNyevtnpV1h7UraUumdDJNukQnb3Pc\nSUT6vkzfHuJCdkPg1W9yzRtvPvPGm9g64/rPP1TpO8PFMdKK7Ia+dRNuPP8fGCLbosJPL5Hn\nsMBBREQUJIwsO+/fUW7ESN6e+X1STQB/aK48eO+GRwB8caYigORtebAgeXumkVqJcQbLN5rZ\nDc3FtvHGmfLZtAYfpY5vwH7xSJ3dOJtwEUDDWGs44sF97Y0hLlwxVs+RbTP4KQBJm7PEvaLI\n0n92lPhCrrntjF55doPdLkLTpsnZAAbMc2INT/qe/n1RSnyR8b9KzG4QuYwFDiIiIn+0emwO\nUNW1HIREXj4QOzIs9pst6NcavkuqBeClVrkAjq6/BCA8IgxhpUlbM1vaPtCdcxbZDRFDaDXA\nxTalbjq+NRMIb9HXut9E7PsIi6wgDWeVshsGO4mqJ9SotwjBjYkzcvpv2ufv5gLVigrC/5ZS\nMHGzK8NQTCl2jGxyG8DK1EruPxWRvt0f/wKg64THFLdL2Q1F59Hhje8AWH2qouJ4Rcsbpwz5\nuE5KfJG9e1lGJDIXCxxERESBRL3fwaHjsj0I6tV4895RzXtrP1BkNxQHS1+37F8vyfCcVIWT\nu9P/faYGgJHLlWmFc0cvABUApO5OB9Cka7S9J/HDJYG9Ph2ClN1Q89AiR0p2iA/MT99aN7/8\nsWVe8vY8ndJMWe3G795hMpGiDY3EtezGyd3pAJra/wsbrLZOvw6g78zy3S7bZlwH0GfGwwD2\nLP4FQJdx/KtE5CUscBAREfmdpC2Zt4prAPh8VO47hjcsiGCF2Jugpl7GOGXSlvJeCdJLtHSj\nP4iOao/ebdDmWVHg8AT96SQiyXI24WLFqnZLEl8dvgAgLNzQ71EuFy/UzT7c9M5ntcTJlJY4\nGJviFM1vUOe7lmc3jLw5vMRNLlNnNxQUU2PV2Q1Bkd04sOwKgA6jH3fv7AB+sInMxgIHERFR\nIHEquyG4WdrwhKZdo5t21b6rQZtnxRea2Y0TO9MBNOtuvUvkWcIjS2FnnTCzWxGA6buqqu9y\n34mddefsf8j69Y7y7MaZLy8CeONNo4NI4IFFjvrivJhEY/iFSh0fQu5xIY1lLnP/ZfBtdsOH\nVTB5dkPoI+td2mWcgwoLEZnLUlrK/8DKWSx8Q4iIKACIiSewALJVyoJ+hbCNWhiRuCELQOtB\nrje8WDSwAMD4ja70dHCWiQUOc1dEoqhR+sACQAxbkRc4xNaVStUfAGjY3kHhQz3DxSD5d3R8\nW6aoUUitVUSBo7QECKiLxvZ+TKf2pwFo7Py75D88UeAIqbRL/NxrAHpOeQS63/iy4XkARq9m\n206ikMAEBxERUcj5dGg+gPedmcaq7/ctcpPjckV+wbU6i0FSaUNwuDgUpQ1rHSSiFLoDU5K3\nZ8C28YQYoSqfM6I+RnKnMKJClQcA3njzmePbMo9vyxQtXUWri7Dw0rvFYRUesg7uPbUvDUDj\nTuavz0X7jBZ9okQVLCU+Q6z6xCQasQ50lr1mDfo2Ts4GMNC2oUNIrcD1+TC7EXz86hO1eFAB\ngHEbvFHzJSIFFjiIiIgCT1iETWZBjB2ZtMWVFIaR7MaxzVkl9y1QNfgQhYPxG6OS43JdeGln\n6bfw1HlUWARKVDNZFSuiY5uygLDwyBKHT1ilzr1ziecbtH5OumVe70Lg4Vfa5t69Ga7ZubN5\nr/oie+IwvgFZdsNhm1UF8R2JAgcAaRCM+hivuX0zfNWY3BFLnR58K9E84ZT4DCDMr9a0fiKk\n3hOR3XDIn7Mb+5dcAdBxrAm9PIhIYIGDiIjI3615PwdA9H8WtxpYD8DMbkUNOzp+1CdD8gF8\nsE4jpmFidkOQFx3czG6sGJUHYNSKmgDOHroIY0UBhwxeLVdEM5qrMgvNe9c/l3he/cAKlUr/\n50TNceut12wtYaWwfVtE9kSkKlr0jXItuyE9/NTeNACNtfqkyissZi13XWvWMHBe3VVjlJWv\nkFqBU2jSyW6UDVVhYw4iT2GBg4iIKPCc3V+nQWyO9MdWA7RTGGFhWPJ2/tgv9MoZ0ppZ55hW\n/bWfX6dqcO7oBcg6hprC2eyGvUdpfsvq99DeO9Og9XMp8RnS1g8Ak7dXWzy4AMDZhIvQHQfr\nFHvZjbOHLgBh9h4l2p2KlmKa1YTlI/IAvLfKG5e13clu6GCVhDzKxJG3+tu7si9XBHBwxc8A\n3hr1hPsvR0QscBAREfm7YZ+KQYbWcYbTd1W1NhnV9cG6GkvezvfkeXnEreLypbtr2Q2xkyU8\nstSbYx1EduNsQrb4Y5Va9zUPk5dLXOhGYR1he+hCeMWSmzcqJG3Oiqz8AEDTbtEGl2Qp8Rm/\naYT/d9rkCI+J2KSDgpgiuyGG1IoCBxGZgkNDbHCKChERBZnPRuYBeP7lwtYDlQkFxfwU0VBT\nTADx4fLS/Zks8gKH+Fq039BpL2q6c0cvFN+IRNmVW80wiJGVfMqODAAxPeqLGSiiUaiQtDkL\nQGTlBw/uW1A2SqZp1+ivE88DeE3WIgSyy8j+Xz5Qn6FrvVcoBJmYvCCiAMUEBxERUagQvUhF\nJV9d73BH4sYszeeUrzeM7IWBbWkjJS4DZYUJ4ytzf1gJN2jzrKgpSKrUuffVkQuvty3fs2O8\nynDm4EUgXHFjy7J9Q2L9Ly3qFKUNhZie9ZO2ZCZtyfTQCI95fQoBTN5WzeVKij8XX4iIyM+x\nwEFERBTM3l0pWi3UTInPiKiI+3fKN4Ao5qeoG2oGAbOyG65sJ5G9ny36Rn115IILrxvTo/6Z\ngxdhm92QiIEpmjNrdU4m4PhDxSqgBUQE5uMBBQAmbHJrtCqzG0TEHRk2uEWFiIgCy+fv5gJ4\n5zPHrRw9ujfh2Kas8IhSBFeVRP6O2Xv3vj52HsBrrfRCEw6f3PhdCmUFDh+851M7FQOYs+8h\npx6l/tZS96QDaNIl2sRzI4VgKnAs6FcIt0c1EVEQY4KDiIgoJLhT2jj8xSUA7d5+yoXHKhYk\nmqt3dYMJNx3fmhkWYbSZiJFX90Rh6Ptjtb4/VjBhU/Wlw/IBjFmjbPzpsNIhL23Iix0nd6UD\naNot2uHKVjrS1W/CibMlX/G30saYlrcALE2qLL/RzexGgBKb+yo+9CD3UiUAnd5/TD7k6NCa\nywDaD3vSp+dIFGBY4CAiIgpg+tkN/QmFJlLPWD26/hKANoNtaiJJWzLDIwEgdW8agCadnzby\n5JopCYPhBeMZh4RVlwHEjihfS8jX6vZW7y5kN6Qn//5Ygb27nHqq5O0ZAMq245RbOiz/t41d\nOTcjnM1uCOpvjdmNkCJW70BFl5+B2Q0i0scCBxERUQiRpnLoH3ZiRwaAZmWHuZbdEBQLkgf3\nLC37RaXuTbt2vsquhVe7TXwUzmQ39M9fakoqtTIVBQ5Nc3sVvto+B1BXBrxEumQtshtSe07p\nAM1Kx+JBBQDGbZBf7rYAEPNiBZHI+HtK/v+eqqkOhsiZkt0Q9PfyCEx5BDExsKms6Y8eRXYj\nlKkbM4vshsDsBpELWOAgIiIKWvrZDY+uNhXZDUGa3NGk89O7Fl418jz2mlzIQxmzuhcB+Ghn\nVfXDHWY3Tu+rC6BR52x5dkPNtXdJnqARTUblU1QUGnTMBgDY1INEpalC1fsAGra3+1hN+qUN\n14jPjMA6BTlLvnonffK9KkRkHAscREREQcje3FZ72QfF8c0cRTyMv6Kg7kkpshs6krZmAqj2\niM2N+tmTyjXvAzA4A3VKXDWg2ul9xTCwVj935AKAohuRLR2NuTVixag8AKNW1BT1gvDIUsBo\nkOQPrXIBAOUJDktYKYB7t8NEqwWHbTW+OnwBwOvtnCuXGMSqR8gykt3Qd3jNZQDtQim2cGp/\nGoDGHa2b9TZOzgYwcF5dX54TUYBjgYOIiChE+edaVNE3RJ7dEM0m1FNRpezG7QKnf7GZucdQ\nL4mbea78yiS+i7JtNc+i7LuTlyfCK5SiFABK7oU161FfEauxV2lKic+Qjkn7a1UA0b8vAnD2\n0EVYwjQfYgr5Z+bUvjQAjTspG6lwHwqZZfvM6wB6T3/Y1yfibdO7FgORM3e70umGKMSxwEFE\nRBSE7CUp9I9fMjQfwNi1rmxt0H9F4z0pSx5YkwwuZyWMxDdcfGb34htfHb5QpRZu5kaOWmG9\n1h3Ts37q3nRY0KRzNIBzRy9UrolbusWUZt3ry/eJSIxPynA2uzG1YzGAOftNWGux6uEdJg7H\n8aaQym4IUnZDGDiv7qoxuavG5AIVfHVKRIGOBQ4iIiLyGfUc0/DIEvkBx7dmAhBNQ9XZDQXN\nvIPDUalGKB6uOZ5GfqqQdTxVbKtRPEqUNuRietbXX6AqygTPvSqmsdQB0LD9Mw6/F7OosxsC\nqxhklhDMbgjMbhC5jAUOIiIishLZjeRtmQCamzdc9timLKhGySZtzgIQFqk8LLwCXDsH4xNh\nFU7szABQWgrIenwkbsgC0HqQE0GYsIhSiwUndmTY21eiE504m3ARQMPYZ1PiM1LiM8LCjb+s\n0Wm7Oub3LQTw4dZqmrtLFNkN7kDxfwGX3SDJiKV6k7+JyCEWOIiIiMg5ohIh9pJI01I0qxhq\nM7oWAZix29o1Qx2sELckW3tVWAMR4o8lJYBqB8q5xPMAGrRWjllRPKFBDrP9J3ZkpO6pC9SZ\n5ah5R0yv+pAN3LXXQERTQCxQ32l2G8DnJyr5+kSI/NSRtZcAtB1qaMz23k9+AdD5g8c8e07e\ntW7CDQBDPq7j6xOhEMICBxERUaiT9laUxRasFQSn1uQ6NKseLfsrb5QfZjC7kRKfUbnseqeU\n3UjakgmtThyzexYBmBZfVXHMl+sfAbA0qXLS5qywyg/EwcazGyd2pgNo1j3anVBDw1jr1hLv\nJCNEaKVZd+trfbi1mvFXZ3aDSG3/sisAOo5+3LenwTksRCxwEBERkXM0CxaKGye9eRPAgi+r\nKA6TshtO0al33MqNdLjkFrtXgNr6h50+UBuOumk261G/WQ/9p1EeL76oVP2BdKPLPSD1t4c4\nu3nk9P40AI06urjDhdkNIn0GsxtCkGU3BGY3yPtY4CAiIgp1Ul9MRWzB/eyGy46uv4Sy/S+j\nW9wGsOy4xnJaczFvb4qKyG6oj1maVNmpcyvrl2HNXDTrHq152LkjFwA0aOvcyBITndiZXlpi\ngda7JLIbosBBRO7zeXZDYHaDiAUOIiIiMp86u2EKe9tP9BnsPGrKJFR7pIIIHPX4gJ1xMDB7\ne4jL2Q33+4xKI2ZcfgayZ+GAAgATN1X39YkobZqcDWAAV+BE5EkscBAREZHfkXqXwk52Q90f\nxPTpHmcPXYTW4FV5qUKHfnbDE7NIFM9mL1pCfihxYxaA1gOdmNpDRERqLHAQERFRwHA2uxFw\n7GU3fE5ekZEqKce3ZQJo4fxEYWY3PMcPsxuCwewG5xALH3UpBuBwWhMRKbDAQURERIFH3R/E\n9BWROrthIh+u37iA9EPMblAQ+/zdXADvfFbL4ZFE7mOBg4iIiNzlWmsMn3M5gOCy1D3pAJp0\niZbfmLDyMoDYkU8afx7vFyk0X8ubbx2FCJbeBGY3iFzDAgcREREFEl4MdBMXkEQBZ2rHYni4\nEbInrJtwA8A7n3FYLHkPCxxERETkLh9mN2Z1LwLw0c6qmveeOXgRwBtv2Ww2kYIb3g8gKLIb\nglPZDcGfixTcAkPk0JCGdwCsO1vR1ydCFGxY4CAiIqJAopPd4CgKNSPbcKZ2KgYwZ1+AXRwm\nCmKHVl0G0H6EtfoZcNkNYcjHzG6Qt7HAQURERAHMXnZDUGQ3hIDuHJG8PRNA894a38KJHRmQ\nzWE5sTMDAGDRfp5tmQCaO/9W6JyAwOwGuWD7rGsAen/0iK9PxCg3k0qK7MYng/MBfLC+huKw\n9RNuABjMMgGRYSxwEBERUcATXU5bD3S3crFiVB6AUStqKm7XbA4aEDSrOatG5wLVnn+5UPyR\n2Q0ifyOyG6LAQUTGWUpLS319Dn7EYuEbQkREFHjMGuPiQoHj1N40AI07P+3mSyssG54HYPRq\n5ZmYYtXoXAAjltUC9/WEAHZFIaLQwQQHERERBTx3ShtnEy4CCK9QAmDUiuc0j/F5dsP9CMnX\niecB3MyNjOlZX5Q2iALX56NyAbyzIkg+yVunXwfQd+bDvj4RooDHAgcRERGFhJO70gE07RZt\n7tOant0QPJTdUBPZjcDdg0MOMbtBRKGDOzJscIsKERFRsLJX4JjepRjAzD02fShSdmQAiOkR\nKivDlLgMADG9QuX7JSKioMQEBxEREYUE07MbgUuat/Ln5J8AvNr8eV+fERERkQlY4CAiIqLA\ncHxrJoAWfZXtNo5tzALQytU2mYrshhA62Q2B2Q0iIgoCLHAQERFRqDt39AKABm2eFX/8Ouk8\ngNdaajccDQLNyso3vs1uSEESH54DEREFExY4iIiIKDCosxuCTnaDAzI9an7fQgAfbq3m6xMh\n8kfrJ94AMHhhHV+fCFEIYYGDiIiIgtyfk84DKC3Fa620QxlSdkNQZDe+OnIBwOttbY7xoaCp\n2jC7QRTEFg0qADB+Q3X5jXsW/QKgy/jHfHNOFAJY4CAiIiJv89qVf1EFEAWO0OTRagizG2SK\n5O2ZAL5Pqglg4ubqjg4PGO5nNxb0KwQwaUsw/EX74oMcALVZ2SAPY4GDiIiI/FTSlkwALftp\n70wx7lX3umncvxUmfX3u6HkADdr4sj1HEGQ3KJQlbswC0NrVrsDkaZ+NzAPw7sqa7jzJ6rE5\n1Wpg+JLa4o9rx+WIL5jdIE9jgYOIiIi8jVf+vSYIqiHsRRr0mveOAtC8t8Zdh9ZcBtB+2JNe\nPiWfO7ruEoBJW57y9YmYxmLB0MW1AWycnA1g4Ly6vj4jCk6W0tJSX5+DH7FY+IYQEREFua8O\nXwDwejtrT41zRy4AaND2WQRRe4tgwgKH9x3blAWg1QDfhyxCvMDRZkjwFDiWDssHUOexu/fv\nWcACB3kMExxEREQUGHR2rLAwEcRY2ghlIVLa2DbjOoA+Mx6Wbgmm0gaAzVOzgUgAKEVERGnf\nWQ87egSRi8IcH0JEREQURF5v96wlvPRcorXzaIO2zzYom5AS07M+qyRErQb7huqNAAAgAElE\nQVTU84f4BgWTWg/fG7Omhq/PgoIfd2TY4BYVIiKigHZ8ayaAFn2tKQ9741pEdaNBa1/2CiUi\nChrbZ10D0PujR3SOEbtUWOYgj+IWFSIiIgo5+qWNr4+dB/BaK5Y/iPwId6IFqB3zrgH47my1\np5gKIs9jgYOIiIiCh5TdEDiuhYjIC/SzGwD+8w/FAPrPYW9R8iwWOIiIiChInN6fBqBRx6dT\n4jIAxPRy8UrvN/vrAnitlYmnRhSEFAOJPM0Psxt7P/kFQOcPHvP1ifi1HpMfgbXPKJHHscko\nERERkbbEDVmJG7J8fRZEoWv3ol92L/rF12dhjpO70k/uSvf1WfhGwsrLtZ+4U7XWfVESIvIc\nJjiIiIgoSDTq+LT4QpHdSNyYBaD1QJv93+eOXgDQoI3GxefRq2t66hTJW9ivwSxSMEp9l9ey\nG36L2Q0if8MCBxEREZG21oPYE4/Il7qOt6kgBHTdqmm3aF+fgs/EjnwSZT8+Io9igYOIiIiC\nnJTdmNOrEMDUuGqwk91QC+gFVSjjj8wsmtkNIiL/xAIHERERBRvROMOd/MXxrZlQzWQhIt8K\nhbrV4TWXAbQb9qSvT8R84sf3UediALP2PuTr06HgxAIHERERBRKXIxUnd6W/3sGaEpe6ckzr\nVAxg9r7yX7XPJZ4HAFQQfwyFBRURedPiQQUAxm2o7vIzJG/PBNC8NyuwREoscBAREVGwMZjd\nOL41s1FHnN5fR30XsxtEpG/P4l8AdBlncp/RoMxuCLO6FwF45le3AQBMcJBHWEpLS319Dn7E\nYuEbQkRE5O/OJlwE0DD2Gf3Djm/LBNCij91SBfehEK+EB7QDy64A6DD6cY++StycawB6TX1E\numXfkisASh5Y4IECRxATBY6o524DGDC3rq9Ph4ITExxEREQUzI5tygLQaoBNpiN5G0sbROQW\ndWnj0OrLANoPD9oIhps+2lkVwMr37vn6RCiYscBBREREAcZhdkMQ2Q1R4CDSJLIbp/engeNC\nApCnsxuCPLshdBrrjdd1mWZVlyhEsMBBREREwSZpcxaAlv3roey3/LOHLgJo2N5aGYmoVCK+\nUOxQOHvoAoCG7Q1NkKUQwa1MpDam5S0AS5Mqy290J7sxvWsxgJm7g78zxcjltXx9ChTMWOAg\nIiKikNOkS3Ty9kxR3RDEcJYKVSy+OynyGXV249TeNACNOzPTERI2fpgNYOD8IOkKwewGhTIW\nOIiIiCioHN+WaQlX9haVshsK7C5JDjG7QRsnZwMYOK+8AqLIbmg/6sMbAAbO15jTpBYK2Q2B\nnUrIo1jgICIiouCn3pQuShvnjl4A0KDNszE963vopUU2xHPPT56gzm6c2JEBoFmPAPg5rhiV\nB2DUipq+PpGA4U52w+BQJ4M2TckGJ4wQuYEFDiIiIgpUpw9eBNDoLZulhXou7PGtmYAFQOru\ndABNukYDmNenEECT3hpPe2xzFoBW/RnzJqBs3nB42W/NJ3enA2jaNdpnJ0ReJ89uOPEoY9mN\nILNn0S8Auoy3Oz23/fAnl4/MWz4y772VrMGR+VjgICIiomCWtCXTYkFYeOm/vqr+u5g86fbX\n3roBoOS+RtONiAolJp4AsxvBISCyGwKzG95U8EsFE5+N2Q0iN1lKS0t9fQ5+xGLhG0JERBRU\nkrZkAmjZTxnrENfhIyuVAGgY+4x8nAo3lZARihE8kO148tk5kRdtn3UNQK3H7wJoO/QpX58O\nEQFMcBAREVFwU5c2PhuZB+DdldH2HsLSBpEX7F92BUDH0Y/7+kTcwtIGkV9hgYOIiIiI41TI\naer9TcxuhJTeHz3i0effPDUbQP853LRC5ATuyLDBLSpEREQkOX0gDUCjDsqBGkRqSZuzALRU\n9aZlz9pQc2D5FQAd3nM3mcICB5ELmOAgIiKiYKZulOCUezfDUuIyYnpx0wo54eSudABNu0X7\n+DzIPYkbsgC0HuSb4pRmaSNu9jUAvaZ5NjziaavH5gAYvqS2r0+EghALHERERETaGnV4OiUu\nw9dnQYFBnd04vjUTQKv+5cU19q8NBe5nNxYNLAAwfmN1M06HKLRwR4YNblEhIiIiI7hSJYdE\ngQNAi77WGgc/NmQECxxELuN63gYLHERERGQEV6pkhKhxSAUOp6TuSQfQpEu0qWdE5Evj294C\nsOhIZV+fCAUtblEhIiKi4JG4MQtA64Ee3zPP0gYZ4Vppg4iIXMPAgg0mOIiIiAKX6Jdx706Y\nvQKHOIAdQ8lXGPwhh9iAk8gdYb4+ASIiIiIzWcKsgzmJ/FPqnnSx/YSCxsEVPx9c8bOvzyKQ\nLOhXuKBfoa/PgoJQEG5RsVgs6huZyyAiIgp6IpqhU91wP7txfFsmgBZ9uO+AXCGyG6xukA5m\nN4jcEWw7MjIzM+vX1/jdxeC3yS0qREREwcqU2oT6SU7sTAfQrHu0O09LRGTQ2nE5AIYutqmD\nJG3Ogtas4h1zrwHoMeURb52dIbsWXAXQbdKjvj4RCkLBuUVl8eLFpbZ8fUZEREQUeP6c/NOf\nk3+S39KiTxTjG2Su1N3pqbvTxdcp8RmiT4emlLgM0UfGtxJWXU5YddnXZ+Gi5SPzlo/M8/VZ\n/H/27jw+yjLP+/23kiCLICCyQ4hL23Oenp7uM2ee83TbdqvsKLigSNgVUHAXxB0QBVdc2hVU\nBIGEsLhH2RJAum17ntczZ3pm+jUz3bZgUglb2DcVhOT8cSU3d1Xddddd+5LP+y9SdVfVlaKw\n+/rV9/r9ACRLrh1R+frrryVdfPHF6V4IAABImy8+3Sbp0qEX2m9MUmGC7AYQp3mjj0qauaJd\nuheScUof3ytpzGOd7TcGZTeM0OyGkWnZDYPsBpIn1wocxtlnn53uJQAAgKz3i/4/SvcSkPsu\nH1Fk/dl9wEqGDAAadlvPdC8hdne/0SHdS8hZK+bWSRo9KxNLKmg+cu2Iyp/+9CdJnTp1evvt\nt30+39VXX71y5cp0LwoAAKTUpUMv/OHb/C2rqyqW+yuW+9O9HABuZq5oR3zD0ZjHOgfFNwC4\ny80Ex89//nPzh/Ly8vLy8v/8z/+cO3du6GWO81YAAACADGfadtjTHzns7fsPSLplfnaMF/n8\nvaqv/niOpFtfyI4FJwrZDWSCXEtwzJgxQ9If//hH01v00KFDZWVl8+bN27x5c+jFDSFSvl4A\nAJB4G5f5f/g+L79FQ8u2pweMS3VPUPc+kUACff5eFUNns8Lzk448P+lIulcB5L5cK3CYOsUv\nfvEL82P79u2HDBkiac2aNWldFwAAyFlUNJBim8qqT5/yXT6i6NRJ36mTuR9JvmX+udkS35B0\n+Q1Fhw8UHD6QyKT8M+OPPjP+aAKfEMhV2XpEJfR0Sbj8Rfv27SUtXLhwwYIFSV8WAADIAAPH\np3OSa1CfyC/Kt0u6dNgFaVoOkPsW3b9f0uT5nbxc/NodhyTd+XoSu43ev+ScoFtmvBN8S2KV\nzNkraewcGnagucvWAgcAAIAXG96tkTTopt6STM7CfVBFDBL+hIA76yPXPzPmqiAea9+ulXTl\nLb1crnloWe40YX1w6LeS+o/cl/rzg2gOsrXAES6vcfXVV5eXlx86dMgENyTV1dVJev7551O3\nOAAAgCZkN5Aov/vwG0m/ue78cBf8dsphST+9/JCc6m6bV1VL6jsyB2siHrMbRvKyG+b9v/fN\n9kl6fhdpz26smb9b0oj7u6V3GUC2FjjCGT16dHl5+bp164qLiyUdPnzYtBft379/upcGAADS\nwGQ3DKIWyD3rF9e0bHda0olj+VJyz0EgTuULdkgadltP82PpE3WSxsx2y27knmc/bSNJIr6B\npPDl2OiQw4cPjxs3rry83H5jWVmZqXdE5PPl2hsCAECztXGZX2ntx7F5ZbWkvsVUVZBE9gLH\n4Jt7R7weSbKprLpl29NyDW0FFTgm/+qEpEV/aBnnSz8+8pikx1a1jfN5gByQawmO9u3bL1++\nfN26dStWrCgvL586deqIESP69u2b7nUBAAAAiTd4YvMtanzx6TZJlw69MN0L8aT8jZ2Sb9jt\nPdK9kNi9cfdBSbe/0tH9stXP7pF044NdQ+9a/NB+SROfieJIERCVXCtwSGrfvn1xcbHHyAYA\nAMhV6Z2lIrIbSJP1S2okmZGD9iNaRlStdresrpJ0xY1FiVxfWr0w+bCk+xYlvk1G0Fs6+/rj\nkp54/2yXh4RmN/6wdpukX10ZXcmG7AZgycECBwAAQCZY906tpI59vvtF/x+ley1ovta9U5uX\n3yCnYkdkvsSvJ4ESnt344tPtki4dmpTGwOcWfpeMp02liNkNwzG7IenTBTu69NEPJ/M+fHnX\ndfd0T+jSgEYUOAAAAJKo5k/tav60m+ECSBnTieOfK//meG8zb7XrMbvx7IQjkh5cGnvTVvfs\nRjjRZjeyyHM3HZHa/Y//dTTdC0GOo8ABAACQFEMm9VLT9EQgxazc0Nnn/iDX4bIurhhRlMAl\nZZrQFh5xZjcqV1RL6j/auYT0K6e8yfMTj0iasbi5jL85eqCFpFGPdkn3QpCzKHAAAIAcZEao\nmK2d474iZchuIL1cShvrFtVKGjI518aUvnXfAUm3vnBuzM8QT3bD7tkJR/7un45Jqv2q9R2v\nNp7v+O2Uw5LufTM4S7Lo/v2SJs/v9OodhyTd9XqHhKwhQzzwrnlLm0spB+lCgQMAAOSUihJ/\nupdwxufvVUm6/IaiNK8DQIg4W3iUzauTNGrmmTBCuOyGi+aT3QBSgwIHAADIQb48DRib5ikq\nlqjmVgDJ8PuPt0v69TWNRzByNbthxJPdSKwHl57z7ITGP1g3hmY3jMnzG4ennvohs5u7AhmM\nAgcAAMgpA8YWZk6Iw2Q3TIEDaCY2LKmRNOjmqIe2ZFcp0J7dcPeLa/Zv/WD/ZcO9tkGZ9pbX\nKbav3H5I0t1vZPphlnce3C9p0rOd3px+QNKUFzOlAoXcQ4EDAADkmszJbhjZsmFDDrOyG0au\nZjdSw3QODtde54VJhyXd905jkeLBpeds/WC//YI/fLZN0q+uSkVvoDfuPijP412T7Z0H9xe0\nIJ+C5KLAAQAActb6xTWSBk+M+ptkoDl799F9km568rzNK6sl9S0OqNB9Ub5d0qXDwg4ciSG7\nYWR1KXDNs3skjXiwa+hdLtmNNc/tkTTiAYdHvTn9wJGDBZLuXxK2T0f82Y34B+K6e3vGAUk7\n/S0l9Tr/hIhvIMkocAAAgJxizqdkWogDQM6wZzdeue2QpLsXnCk0WNmNcI7sbpmkhYVKUnbD\nOnLi8frHVreVJLVNxmIAOwocAAAgZ5HdAKKyeVW1pJuebExSBGU3jHDZjTXP7ZY04oFmOhfZ\nMbtht3TmPkkT5p0X8Cin7Mb6JTWSpryYiv98JS+78dKthyVNe4u8BlKKAgcAAMgpZDeA2Px2\nyuF/6Btwy9b3v5F02fVeu2M2E/YmGvbshkdDJkVugHJoV8uVT9Wd+DbPKog8Pe6opIeXt4v2\n5ZIhKLthHWWaP/GIpPtts2+Xzd4rafwTnVO7QDRfFDgAAACSLua5EkAq/cfmDqFDTH/30TeS\nfnNthDJH8rIbH7y4W9Lw6XE9f5wjWuLvDBqU3XAx+ObeK5+qi/mF0uWNuw+e3VbHj+WbH80s\nGFPgAFKGAgcAAMgdFcv9kgaMI8QBRC2otLHunVqpxZBJvUyBI8iWNVWSrhhRFMMLffHpdkmX\nDg3bpjTDJW8AysJpByRNfenc4keCZ9DGmd147/ndkm6YEWORyL08ZI4y/eXLgwqMb8iW3Yi2\nbQcQGwocAAAASTfo5t6byqo3lVVn9ZwINEPrF9dIBaHtbBpOBwz7dJy3kihxZjeMOP/peSlq\nbP3gG7nOTEmLxQ/vl3RO8gsLXhqaxpmjASKiwAEAAHJHZmY3tqyukiT53C8DMoppFWFmLSdW\nWrIbSx7eJ+nmp72eE3Hx5rQDkqa8lPj2mVOT8JxGzNkNw6Uk8cGLuyQNn97duuX1uw5KuuPV\ngHqHyW5sKjsWzzKAiChwAAAApAJfWiIb1Z9yLswFhTXMj2sX1Uq6cnLkJprZrmO3H1Y/u+fG\nwMkpsWU3yhfskDTstp72G6NNxJQ+vlfSmMccenlOfDo4vOE4z8WjhdMOSpr6kltYo03b0+Hu\n4j+DSDYKHAAAAMl1xY1F6V4CEBeXkwVr366VdOUtYYsajt/nu/PY1jQqsWU3HEs2U146d/Wz\nexKzrOxnz26Es/qZPZJufKirpIX3HpQ09bdRfB4A7yhwAAAAAHB25a291FTgiHxxM8huGEHZ\njXgEZTcMk9145bZD8jaJ1jG7EU5s2Q3DPbthhKslLbp//+T5NBlFcvkaGhrSvYYM4vPxhgAA\nAACRff5elaTLbyhK8zqyxPLH9koa93gUlQhT4Oh+/glJIx7oKumpMUclPVIa10SV1DMzXA7t\naUGBA8mWl+4FAAAAAEACLHlk35JH9qV7FQlz94IO9vjGW/cdOK/bD94fXjJnb8mcvUlYl1dL\nZ+4z/T5umNGtVdvT3S78Po2LQTPBERUAAAAAXq1bVCtpyORe4bIb696plTRkUq8YMguZ5tOF\nOyQNnepwiiRaMb8PJrthseIbn7y2U9LVd/aIc2FeVJZWS+o/JsYWoW9OPyC17nnxdwldFOCA\nAgcAAACAJLJqIsl+oZufSsAU2GRY9fQeSSMfjqtzx60vRDdEduycBJeWQnvNfvrmDklDpzQW\ngIIG6AY1+0hInQhwR8uJAPTgAAAgS21c5pc0cHxhuhcCIFj8BY6VT+2RVPxIwlp7plhCChxp\n51jg2PpJR0nzP2ujpgLHkcMFku5ffI65xnEOLpAkJDgAAAAAJFEKshsRVZb6JfUfk54aaMTS\nxmt3HJJ05+sdJJkZtKd+8EkaPbNL0JUVJX5JA8Y2/iIv3XJ4/74CSf90+ZFr74k8sVWS6Yth\n4hVr36pV06wcR7+dcljSvW+2l9Oc4KFTem795FvrR5PdmD/xiJdlAMlAgQMAAOQCe3YjZXl4\nAKmRvdmNnPfT/+e4JKnNogf2S5r8XKfejc1EGxMcZDeQShQ4AAAAUioHOi8CjoLCBQm3fkmN\npME3947hsenKbnhkshvGjQ+6VXOC3t5pb7dv+uPZXl7oved3n91BN8zoZn50yW4YJrvh4tih\nxh1lhy4OE17eeXC/pEnPMh0WKUKBAwAA5BqyG0B6bVlTJem/traXdMdrHe13mfMX7nt4L9Yv\nrpE0eKJbsWPL6ipJV9xYFPOrfPL6TklX3+F1Uskz449KemhZO8d7HxtxTNLja9p6fLa3ph+Q\ndOuL0fUWlbRs1j5J4+ee6fE5d9QxSbPKvL60ZdUzeySNfKirpLfvPyDplvmR1uOTpJI5exPe\n5RSIiAIHAABASpHdQK7Ky2/s1v/nLR0k5edFbt7/h8+2SfrVVRd6ef7Yshu5xFSO/P/eNmhA\nSagL/t8jX5QfuXTYBUG3W9kNR2vfrpX0H79vH65GE+r2Vzq6PDPZDaQYQ0MCMEUFAAAk2+8/\n2S7p11cHbzyAnPHK7Yck3f1Gh4hXRlXgkNMgj2bFe4Hji/LtkkILHO7Wvl1bf9qn6Ee6vn7n\nQYWkdZQr42OQRUhwAACA7Fax3C9pwLiMPmAPNCuOpQ1zYGTbv7STNPm5xi/2vZc27NI7EiWN\nrhhRtHTWPnMGxPLe/N2Sbrg/IEARbWlD0pvTD0htel78nf3GFXPrJI2eFTzMJVTBWQ3LZu+V\nNP4JQmpIGwocAAAAKUV2A3Bh6iAnjufL6UyKyW6YAofd5pXVkvoWx57s+OLTbZIuHRpFweWF\nyYcl3bcoQhvOUJlclo02u2Hc8VrHN6cfCL2d7AZSjAIHAADIbpm5SQAQZPdXbWTLbsQjXHZj\n64ffXHbd+ebPH7+2U1KHHickXTa88UarZabHozEfvLhb0vDp3SSte6dW0pBJAT2Mf97v0Kay\nQ8f2tZB0zV2Re5E+NfaopP85yO2aN+4+KFtviyAT5gYfTrGyG+ULdiiOsayde58wf5gz4pik\nX151UNLoWWH7ngQlR6Y0NUN9euxRSQ+XeO3iASQQBQ4AAAAAKfL7j7f/+poL3rjnYLei7yUN\nn9Y96ILYhp70Le6z9cNv4llYVNkN475F7TeVHYrhtexlWY+DVM0I3h3/1UbSTU9FaMARm9C/\nCyDr0FMzAE1GAQAAgIRbv6RG0tnn/iDJvcAxa/hxSXM/ODvOV4y2fWm6hBY4FtxzUFKfnx6X\ndOXkXhuX1kjy5TdI+u5QgaSr73SIioSOhjVennpI0j0LIzd8TaB3H90n6aYnk1KIAVyQ4AAA\nAACQCr++prEBze0vO5+/SLh3HtgvadJznZ6fdETSjHfOSdILrXluj6QRD0TdciI0u3Hbyx0l\nrV103H7jgLGFkj55bafHp1397B5JNz4YYT0rn9ojqfiR6JZtNXld/cweSTc+1HXuqGOSZpW1\nlfTpgh3n9dK+2pZRPSeQEBQ4AAAAACRXaLtQu8oV1ZL6j+4jp+yGl9GwJvVgSgOVJX5J/cde\nKOkvW/fHs+xw3NtkJMSVkxubfQyccOatc8xuGKHZDcNkNxY9sF9xNEBZ/NB+Sb68hptdT8fM\nG3VM0s9/E9uLAAlAgQMAAABA6nz+XtWuv7SRNGpm5OGjdjGcOpnUtKWPJ7thzte412hCsxvh\nzowkipla8t3xfEn3vhk8ySVidsMcjTm7vc/9MkdWk9cbH2p8FZPdMAWOobf1NEdUgNSjwAEA\nAAAgnfqP7vP5+1Wfv191+fVFofe6ZzeM22xnXvqPTcxkpfI3dkgadrvDUBKT3Yh/2qu9QcYL\nkw5Luu+dqIfOehFtdmPd4lpJefkNgyb0ljTxmcaH29+T91/cLen66d2sR80sa/vxqzs/fvXI\nTU9GHigDJAMFDgAAgDRYNnuvpPFPdE73QoBUu/yGotge6JjdSEEzUffshqOK5f7uF8dS+yh9\nYq+kMbMj/5fBGssaKmiAq7Ho/v2SJs9vLFVEnNsS0Z//2FbS9ZKkET/7QdKaf28R53MCcaLA\nAQAAACDNrOzG5pXVkvoW94mqchGxT8eKuXWSRs+K4lCMY3bDzqpfrHyqTlLxI9GduFHgcBOT\n3TAFDstb9x2QdOsLAbWMt2cckHTL82ELHPEbMrGX4+3We/Ly1ENmL7mprNr+tl9zF9kNpBMF\nDgAAgDTo/ffH60/5Nq2o7jc6cvweaA5M586/uyS6R5kKiClweFcyZ6+ksXM6S1r7dq2kK29x\n3tLHJmJ2o+zJOknb/6v1o6XtZMuhRMxufPL6zq7nq0Wr+nXvfPvVv7SVdM+CDpL+sHabpF9d\neaFCshuGld2Q9NlbtZKuujX4V1719B7rzyMfjtDFo2OnU39/2WHzZ8fshnm2iM8DJBAFDgAA\ngFT7ct22lu303cECSV+Ub5d06bAL0r0oICP0LW4s+UV16iRin45w2Y037j4onV300+OO91rc\n4ySO2Y3QFhXGBy/tkiTlu7+ipC/Kt/+PyyP/x8FUKzrEVJ9x6TNieXrcUUkPL29nv7Epe9LB\n6RFA2lDgAAAASI9WHU5JajgdyxQDIPfYp66aMRw3PRndCBIvA2UNk92wuMc3ls7cJ7W/6JeH\no1qMu1GPBtRE7KUT0yzjv//c5ro7HB549R1nzoAMmSRJn711TNJftnYIaqvxx/VfS/rl4ItC\nn+TktwHllc/fqzKNUUY+3HXlk3WSih/1dNzmy3XbJF0yxLnuQ3YDqUeBAwAAINWs/cD/3vS3\n7w7Qlg8I1usnJlKRrBmrxh8+2/azQV6jIvbLls7cJ2nCvMblOR5yCc1uGMOndfe4vA9f7/7C\n+tYRLws9aRLEamti3XLdvY1rGHZ7z8/fqzJ/3ri0RpLU0vw4fdB3kl7c0Doou2H3l60dJF0y\nJOIagRShwAEAAJBSW9//RtJl158vaf+21pJadziV5jUBmefPn3f48+eHp70VxdhUL9kNL37/\nyXZJv7668WyIVcgwXp56qENg4eXrf22bkNc17M0y4uGY3bAreXyvpLGPFdlvLH60y/rFNesX\n13ipLh0/4nbQZtED+xX9hFogHhQ4AAAA0iwvv2Hr+9+YkgcASf1H9/nz54k8EuLIPbuxblGt\npCGTHfIRh/YV2AegeLHk4X2SLr70sP1133lwvxIxsdV49Y5Dku56vYM9UbJs9l6pzcnv8rb/\nf/t/9Msjki4b7vyfmoETAqbh/vQfv5UktY55RgyQehQ4AAAAUuqy68///cfbf//x9l9fc0Gr\nc06nezlAhurc46SkrR98o/B7cklfrt0m6ZIro+hIGpHJbpgCR6jQ0sbdCzpsWVW1ZdWhK0YW\nWTd+unCHpKFTI8yaTaC/++URSd4bf459zHliy+CJvR1vD3XXG26vRXYDqUeBAwAA5IKKEr+k\nAWMjjGbMTGe1O13/g+8Pn22LamwEkNtMH9CtHxyT9PS4oy6dIJLEMbsRmy/Xf/3jy3TJ4IuC\nzn3Emd0wA1lCm3rYu4GMf8Jexei06P79f/vjfusUjDVcdvFD+yVNfCZgPaVP1EkaM5vsBrIG\nBQ4AAIBU+/U1jWf7+xb3+eeNX1u3ly/YMey21H3fC2S+y4afb8aUhhOU3bB63GxZXSXpihuL\nIr7EgnsPSrrttx0jXunCnt0wUpbdWP7YXknjHu9sKrxbP/xG0qeLukqa/1kb67LX7jwkqVX4\npqVt2p1e+WSd4/wU003D55MSd6AGSAYKHAAAIBdkaXZD0i8GNjYCLF+wI70rATJTxOzGhndr\nJA26KfLBirfvPyDplvnnhrsgaO7pl+u/lkzyIi7mGcw0kz9vbS/pngWNhzus8kQMTzt8Wvcv\n127b9r/PsW7Zsroqz3UuU+FPzXiaxiLFr5rKQ63aBByXe/X2Q5LueiNsduPRa49LevKjs2NY\nNpA8FDgAAAAywjldT0rasLRm0ASvB+CB5uD5SUckzXjnnIhXqmk+kT7ktYUAACAASURBVLxl\nN4w4sxspFnos5cL/dcQeY/nkrW6SXljX+qOXd3308uHW7U9JGnRT7ztfMyWVM10zrDf2vfm7\nz2qtk9/ljQ48jfLRKzslXXt3j4YGn6TJzzkXhsx82YH8hwsZgAIHAAAAgCzzuw+/kfSb685X\n+OzG5lXVkvqOPDM71iW7YVjZjcYf48tuBPUZ7VvcR1LfYr106+GXbm2cgDvu8c6bV1ZvXlnd\nt7jP5+9VSbr8hqKIz2xdaS9tLJx2QHIrA73/4m5J10/v5nivvbphuod+9IoZpKJb5p+76uk9\nq57eM/LhruaWC378vSTp7FVP75HO6tjjZMQ1AylAgQMAAGSZjcv8kgaOz9YzKeGYORHvv7D7\n/Rd2X3+f8w4EaIY8ZjeMz9dUSbp8RJGkf6vsIKnvyMiPCpdB+OzNHZKumpLmzji//3i7mnr3\nmOyGKXAEadPutKQX1jW22bj2nsaUR+nje0sf3zvmsc71pwKut97YG+53/g9Oi5YN4ZYU1ImD\n+AYyBAUOAAAAAFlg6/vf7P6qjaSRD3fNb1kvae3btZLadDhlKhoZ4pPXd0q6+o4e4fqMmuyG\n5Zs/tZWk4sbsxqt3HJJ01+tuE1jzW9THucgPf7tL0nX3dpdU+vheSWPCTI01Rj7c1V5+XTG3\nTtLoWV1MpmPJI/sk3fzUeS7PAKQABQ4AAJBlsjG7YbZh9tmN4ZDdACJa89yeEQ9cqKZ/WXb2\nSsf0t9srxLuP7pN005NntuLzRh2TOs4saxt68fb/SGkTzT98tk2S1QHUYs1dsqv597Yr/r3O\nfq7ETIQNbblqVS5GPNBNTSNROvWIvJ6rpvR84+6Db9x98PZXGtuU1H7d6uXbDlkdUoFMQ4ED\nAAAgs2wqq5bUb1SfiFcCzcrJb/PP7XXi0K6W1i0uRUPvo1ViYO8AEurqOzwUD2wmPdvpg5d2\nPTj0W6n71bftcs9uGL++5oIV/17n5ck/eHGXpOHTu7/z4P7uF34nSWqtpuyG4Z7d0JlRL91e\nvu2QuWX0rIB2pGQ3kCEocAAAACSdl+wGAEebV1VJaqj3ST5J7ToH9LO02mdsff8bSZddf/7v\nP9kuSWocl/rl2m2SrGac9uyG4ZjdMNxrDc+MP/LQsij6gwR5b/5uhfS/+NVVF4a5PNjRQ85b\nuYg1ncnPdbJ3OQ0SdFzFZDdMgUO26bZG+Rs7JA27Pc09SgALBQ4AAIDMQnYDCNWy3SlJJ44W\n5BU0hP4b2bSiuqBlwC1Jym4Yv7nu/GfGH5G0Yl7dHn9LSW3bn5aHKS3hDJ/Wffg0T1d+8el2\nSZcOvUBS514nPnhx1/Dp3V2ut+4Nagsale+O5cf8WCCVKHAAAAAAyFx9RxZJ+uLTbY73DpzQ\ne9OKakmXXd94YOTXVwd0rLAPUo3W6mf3SLrxwa6hdz207JwV8yIcEnFv3hludolHU1481xw/\nsYT2+gk3BSYou/HcTUckPfCucxql/I2dUivHu8huINNQ4AAAAACQ6S4deqGk9YtrrFtMXaPf\n6D79Rvf56OVdH728yxqMqqYxH90u/lZNJZKYfbn+a0mXDL4o6PbRM7tseLemU68TjmmR3334\nTe9/UM1/hD3/Es7WD7+RdJmtwcf7L+6WdP30bmrKbkgqf2Nni1YadnsU/T7enH5A0pQXz106\na5+kCXMdGmfUbm9Mwti7sXa/8PuoXghIFwocAAAAAJqL9UtqJA2+2dMBFpPd+HL9Ucd7K0v9\npi2IC/f+nVvWVEm6ItYZt0f3F6yYW2f1+wzt9WNlNzYsMYWhgKEwH7+685q7ejw15qjke6S0\nnf2uTSuqrYspbSCLUOAAAAAAkB0GTzxTmOg3+kwnDiu78UX5dkmXDrug9z8cU5gBq47MERiT\nEwlyyeCLKpb7K5b7B4wLnlGd36Kh/xjnwdXhZqy4W/PcHqnNiAcCDsXkF9QHXVZZ6m/dXkf3\nR7ebm/JiY4uQCXPPmzvqmP/rVtfc1XjXJ6/vlHT1HT16Xfi9uaX3T45b7/Ca5/ZICloVkIEo\ncAAAAADIeq/fdVDSzwYG3PjanYck3fnamdkfB3cENiMNNGfEsTlrvB4qMaWNz9dUSbo8phRG\naHZj5/aWkpY/tnfc4xFGtypkVmuQ957fLemGGd0kDbIlVt6ecUCSdJb50WQ3Pnm9MaUyZnaX\nkjl7d33VZuycyAsAMg0FDgAAAAA54vj+xumwv77mgpI5ezuc98OhfS3CXfzqHYfUNAv20qEX\nzhlxLNyVVnajbF6dpFEzz1QWqv6trSSN0NKZ+yRNmHfeH9Zuk/Sr8M1NrSuDbn9z2gEpL/T6\nXdtaB91ixUZemHRY0n3vOAx8DfLRKzslqcEntZTUreeZabuVpf42HbTjv9u8cfdBSV0KTwc9\nNii7sfLJOknFj7qVV4C0oMABAAAAIOvd8WpHSRveDShS9Pi/vu0hvTf/ezVNLbHXJoJ4z254\nsfKpPZKKH4nuWEer1vVTXjr3dx9v/93HR38Tcr5m4zK/pIHjnQ/FBDHZjVC3PH+upFduP+T+\n8AeHfivp2U/bKLDhKJDJKHAAAAAAyBH2gSZj53TevKpaUutzgiMJhsluRCW0PtLjx9+aP1iJ\nDJPdWPlve747WrDk4f3n/9PRy68vsj8kNLthTHnpXMfbv//OIdZhmOyGl8LHn788R9KslY1F\nnC69Thze1+LN6QfONOaYd54Zs9KqrfPbZSG7gYxFgQMAAABATqks9Uv67nDBsNv7SPrszR3e\nH+syZuXRa49LevKjs0PvklT6RJ2kMbMbN//Fj3Rd8vB+SX/94py/fnHAKl6smb9b0oj7g+MV\naxfVSvrz78+R9OBS596ooSWMT17bKanVOZF/r3A+eHHXOU4pE5PdMMhuIFtQ4AAAAACQy6xp\nqZI+eGmXpOHTurtc36JV/aay6n6jzkxpMV0zpOBeGLIVHb49Ery3qg8efuJgy+oqSVfcWBTu\ngo9e2Xn+3+vau3tIWjprn6QJc4PLDV4OrVz8s+Ot2p7++NUj19zVQ1LxI11L5uw1d/31n9tJ\n6j/mzJgVIEtR4AAAAACQU8KNbpV0VuuAqoPVXcIqfAy+ufemsmrrAtNKQ8qXLbthEiL9xxRu\nWlGtwIG1xvsv7JYktZA05aVz1y+uWb/4uJlxO+L+bmsX1a5dVHvl5F72h5gfr5zc+OOnC3dI\nGjq1p1xdfWcPl3tN29TQ3iLrFtVKGjuncQFmAM0f1m5zaYwKZAUKHAAAAACahYoSf8u2OnEs\nf8O7NQps2GFsWV11xY1FVnZjycP7pYKi//vonr+1sV92+pQv9MlN804TsmjbQZLadjjlvp6X\npx6SOtyz0K0ViMluGKdOOryupM2rqiT1HVnkeO+y2XsljX+iq71wI6lkzt6zO5ySdMer3c3k\nFyDbUeAAAAAA0Izkt2ioP+2rP+X77M0dvX9iahDndSr83tz4+I3HJD222tNEldDshnH9fQEt\nNmr+O6BtR1B2Q9LHr+6UZA6PGBGzG+4WPbBf0pw1nSQtm/1d42qbCjetTeWltqV1vT27sfWD\nbyQd3tVS0tV3uCVEgExDgQMAAABALjPJhX6j+gwYWyhp3Tu11l32CkVefkP9KZ+kH//s25VP\nflv8aJebn+4kSeoU9ISDJji0IK0o8UuaMDfgdMzGpTWSpMYCx9szDqgp6yHJPbthbH3/m2P7\nz5J01a09Jz3bafb1x2dff/yJ989+csxRSY+WtpN08tt8x8eufmaPpC5FJ82PK+bWSerY/aRU\nMGRyL90Q8cWBLEOBAwAAAEAzMmRSL0mfr6my33jZ9edvWV2VV9DwD5ccPXHcuV4QTsVyvyQ5\nHx+RpFZtTkvauNz/o1+YDiDBvTzt2Y1wx2ciKl+wQ9Kw23pKmvxcJzUVOIy3ZhxoZyunrFtU\naw7ahEZFLht+frQvDWQIX0NDQ7rXkEF8Pt4QAAAyS8z/Xx8AomIfaPLe87sl3TAjeJirI1Pg\nyCto6Deqz4u3HJY0/e32ksqerJM06tEuyx/bK6nrRd+ZFqeX31Dk8mwJKXBYzKjaH37wSWrX\n4ZSk747ld7/oOzV1Ehk6tWf5GzslDbu9schS+sReSWNmd452AUDakeAAAACZ7svPzv3ys+OP\nrzk78qUAELetH3wTOhHWpe4wYFzhxmX+0z/4Pnpl5wU/1fY/B//HatzjURQLzEt8uW6bpEuG\nBI81McdMRs/q8vFrOyXl5UlNtYmg0obdrc8HZEY2rayW1K/YuYEIkL0ocAAAgIw26KbeX352\nPN2rAJD7THbDqKtpGe6yz97cIemqKWGrCSa+sWlFdZfzw3YhdTd31DGpa7/xexzvPXqg4M1p\nB7qFTHS1Rt5atyydtS+vQBPmnlc2r07SqJldzO39ivvMG3Xsjx8fm1nWVrbshkF2A9krL90L\nAAAAiOCSoQcuGXog3asA0Fw4NqEYdFPvE8fyP3ltp+NDBo4vlNSmwyn7VNdwPl2449OFO6wf\nF047sHCaw3/iQuMbkkbPaixSXHNnj2vu7NH5om87X/RtxFcEmgkSHAAAINM5DiwAgOTp1uf7\noFsW3HOw548k6fghhz3U5lXVQV1G+43us3Dagb/9nwNF/3Bc0uCbo/jvWNHFZrCr86jaKS+d\nOW/yi4EXqakJSNfzg7+9njC3Mc2R3yK4z+DMsrZLHtm35JHvb37qPAG5ggIHAAAAAAQYPr27\n+YM1YlbSjr+1vu3ljuEeUtCyoe9ITwdSggaXTH0peKjKuMc7b1lTtWXN8StGFEWzaqC5Y2hI\nAKaoAAAAALDYCxzuKkv9kvqPKYzt4XYv3XpY0s8HHKTAAUSFBAcAAAAAOPNem/j6X9pK6j9G\nkjYsrZE0aEJv6+Ebl/nV1KpD0qL790uaPL+TyxNS3QCiRYEDAAAAAKL29v0HJN0yP+CAiWmH\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sk6ScWPOjT9JbsBAEgeChwAgCwQ7Sat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"text/plain": [ "plot without title" ] }, "metadata": { "image/png": { "height": 720, "width": 720 } }, "output_type": "display_data" } ], "source": [ "options(repr.plot.width = 12, repr.plot.height = 12)\n", "FeaturePlot(obj.integrated, \"MT2A\")" ] }, { "cell_type": "code", "execution_count": 22, "id": "f8807ae2", "metadata": {}, "outputs": [], "source": [ "DefaultAssay(obj.integrated)= \"RNA\"\n", "\n", "isgGenes = c(\"MT2A\", \"ISG15\", \"LY6E\", \"IFIT1\", \"IFIT2\", \"IFIT3\", \"IFITM1\", \"IFITM3\", \"IFI44L\", \"IFI6\", \"MX1\", \"IFI27\", \"IFI44L\", \"RSAD2\", \"SIGLEC1\", \"IFIT1\", \"ISG15\")\n", "\n", "obj.integrated = AddModuleScore(obj.integrated, features = isgGenes, name = \"isg_score_small\")\n" ] }, { "cell_type": "code", "execution_count": 23, "id": "fa1007e7", "metadata": { "lines_to_next_cell": 0 }, "outputs": [ { "data": { "image/png": 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l8lJ75rNzYsSJV06wN+5RRbPv6fpKwfgyUFyaFs5PPVeyRfuRUABBYCDgAAABTh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JrtxQZdzDY/+7z7eeD16SU2zYsGnu14h1fv7mp6/zSg+6fB38Z8Xbmh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\nyiufqL+v/QNWA5DdsGx8j3Sg31Q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z+vOQk83iTb1j+1H7DoTX+phnniOuRVduO95peBESudp1oKEZq6jc32qdtxY3Ls/uJA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\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Eh9xbOaimc1JfoUAAAghkhwAAAA28s/\nuVeijwAAABLM0dzcnOgzJBGHgx8IAAAAAAD2Q4sKAACwvcqldZVL6xJ9CgAAkEgUOAAAAAAA\ngO3RkeGGFhUAAOKmbEGDpNxRvaP2ggsbJOWOjNoLAgAAGyHBAQAAAAAAbI/AghsSHAAAJIPi\n2Y2SBo1lNwoAAAgWCQ4AAAAAAGB7BBbckOAAAAAAAMCOSHAAAAA7KZrZVDSzyc8FZQsazPhS\nAACQVihwAAAA2yue3WjGdgAAgLRFgQMAANhJwfieBeN7+rkgd1TvvXscJXOodwAAkF72S/QB\nAAAAIsW+FQAAwExNNwwZBQAgmZnpG/4THAAAID3RogIAAJJFydzGkrlBtZaUzGmkCQUAALii\nRQUAANhGMNmNsoUNknJH9o79cQAAQBKhI8MNLSoAAMSIiWbknxTzYRkUOAAASE8kOAAAQEqh\ntAEAQHoisOCGBAcAAAAAAHbEkFEAAAAAAGB7FDgAAECyK57VVDyrKdGnAAAASY0CBwAAiKEH\nz9v94Hm74/mOVUV1VUV18XxHAACQDBgyCgAAYu7FG7dLuuKp9uE9fdC4wNthAQBAmmOmphuG\njAIAEAsRFjgAAAAC4vO8GwocAAAkVsWSekkDh/VJ9EEAAIDNMIMDAAAko+LZjcWzGxN9CgAA\nYBvM4AAAAEmE7AYAAAgPHRluaFEBAAAAAMCOaFEBAAAAAAC2R4EDAAAAAADYHgUOAABgA0Uz\nmopmNCX6FAAAIHlR4AAAAAAAALbHTE03DBkFACD+KpbUi/0pAAAgMiQ4AABAlJUvbChf2JDo\nU6h0XkPpvMQfAwAAxMd+iT4AAABIWcs/Wylp8Ckn+L+M7AYAAIgcBQ4AABBlOSN7x+iVS+c2\nSso7qVcwF+eNidUxAABAEqLAAQAAYiVgdgMAACBamKnphiGjAADYWvHsRkmDxgYV8QAAAKmE\nIaMAACA6imc1Fc9qSvQpAABAmqJFBQAApA6yGwAApC06MtzQogIAAAAAgB3RogIAAAAAAGyP\nAgcAAAAAALA9ChwAAAAAAMD2KHAAAAAAAADbo8ABAACirGJJfcWS+uR/TQAAkEoocAAAAAAA\nANtjK6ob1sQCABCS0rmNkvJO6pXogwAAgHRHggMAAAAAANje3q6NswAAIABJREFUfok+AAAA\nsLG9exyJPgIAAIBEi4oHWlQAAIip8oX1knJG9kn0QQAAQKqhRQUAAISjfFF9+SLWmgAAgGRB\niwoAAIgfshsAACBG6MhwQ4sKAAAAAAB2RIsKAADwp2ROY8mcxkSfAgAAIAAKHAAAIFJFM5qK\nZjS53lO+sKF8YUOizgMAANIQMzgAAIA/+Sf3SvQRAAAAAmPkhBtmcAAAYGuVy+okZQ/pm+iD\nAACAeKNFBQAAAAAA2B6BBTckOAAASDYVS+olDRzGflkAAOAPCQ4AABC+4llNxbOaAl8HAAAQ\nYwwZBQAAQalYXC9p4HB/SQqzUDa6c0nJbgAAgGBQ4AAAAOEbNK5n/N+0bH6DpNzRveP/1gAA\nIGlR4AAAAJJUNKNJUsEEz4KFCWW0aducM9IzSeGd14jDTtnln61se2Dgy8oWNEjKHUURBACA\ndMEMDgAAYDO//NiG+AYAAPDA0hA3bFEBAAAAAMCOSHAAAJCmKpbUmw2s3krmNJr2k+RUPLux\neHbyHg8AACQEBQ4AAJAW/BR0AABACqAjww0tKgCANFcyt1FS/kkxnxUaf6a6wdJZAABSFVtU\nAABA4pXOa5SUNyaGhRVKGwAApDYKHAAAYJ+UzG4AAIB0QEeGG1pUAACIheriWkmZg/ol+iAA\nACBlMWQUAAAAAADYHoEFNyQ4AAAAAACwIxIcAADArtj8CgAAnChwAACA1Fe+qL58EaUQAABS\nGVtUAACAXbH5FQAAODFywg0zOAAAqad4ZpOkQeN7JvogAAAAMUSLCgAAsKWyhQ1lCxsSfQoA\nAJAsaFEBACDFhZfdKJnbKCn/pF7+Lyub3yApd3TvMN5CkqlQ5I4M8+kAAABOFDgAAIAtURYB\nAACuGDnhhhkcAIC0Ujq3UVKeVUzD7BzJGZHUUzxL5zdIygs3PwIAAFIJMzgAAECKKJrZVDSz\nKdGnAAAAiUFgwQ0JDgBACihb0CApd1Ta5RpMdaOAfTEAAKQlZnAAAIAUQWkDAIB0RmDBDQkO\nAAAAAADsiBkcAAAEUDqvsXReo/Nm8ezG4tmNfq5PTqXzG8xITgAAgJREiwoAAP5ULKkP+/cB\nlcvqJP38XZtBYy3WlNhdxZJ6SQOHJd2alZI5jZLyT07BnzkAAPCDAgcAAIE43LoXbVqtYJeq\nh+JZTZIGjWNsBwAAKYICBwAA/gwc1sd0dpQvqs8ZEVpaIXtI36icwSRBovVq0dK8N9En8IHs\nBgAA6YkCBwAAFsrmN0jKHd1bUt7o3uWL6r2vKV9c78iQpIFDk65NIw5CLfckm7YH/ZroIwAA\ngGiiwAEASFNlCxok5Y4KqnHD/4f5iqX1kgYO7VO+uF5SzvAof/JPtuxGdFUtr5OUNdjn91i5\ntE5S9tDAPwRTh7J75QUAAISHAgcAIK2VzGl0ZDTnjektqWJxvaSBw/uoNbvhX/MeR7OUO7q3\nKXDAv/KFDZJyRibLKBBHGxbDAwCQUihwAADSVO6o3mbdRvDMMA7vaZ0OtXxUjmJ2I1XDCGbh\nbt6YfWMy/GQ3jGCyG8bePSHsu0ntXAwAAGmIAgcAIH15TqN0hPb0YFIecDLZDVPgQPzVlNZK\nGpDXL9EHAQAgVhzNzeQz93E4+IEAgI259piE8/Ql9ZIGDkuR0IRzMohCGWOBVEWBAwCQ8khw\nAABSlusnfD+cC1PiUNrw1eSCRAlp1qyt+S9tUP4AAKQAChwAgNQRdnZD0oePb5R05i3HRHKA\nGG1RCY9rZSfZshvlC+sl5YxMih9U+qgurZWU6VLFqC6plZSZT10DAJAKKHAAAOytclmdWgdG\nun6tQNkN51IPM0pjTc3GUN+6qqhWUlaBzw+H3vUOshvJJh2yG/5t/M+hM/+ztvP/hNyiu+Tj\nryQNO/34GBwKAIBwhDBsHAAAW6tcXle5vM7Xo2feckx48Q3zO3AjZ3ifKMY3SmY3lsxOzZGc\nOSP7EN+Iv8y8ft9t2n/ZJyv33UN2AwCQQpip6YYhowCQwkx1I9tlKalH4sOSc0KH856q5XWS\nMvZrlmT+pxGjT4mmupE/tlfAK5NH+oy0sClT3Rhy6glRf+Wa8hWSBuT0j/orAwAQJFpUAAC2\nZzkfsXxRvaScES5zKAYHO4eiurhWUuYgi7KFqYk4WhfKxvQX4PYqbSA5LXxvlaSR5xxnbjpL\nG97zOCLXvNdRXVob3dcEACB4FDgAAMnu46c2SOo6YLfcCxZ+VBfX7neA9vzksxOzpmyFpOwh\ngX/bnGs1NSMr6FpJuklgduP1O7ZKuuihjok6QEIEWaeoLq61LNgpeiuEB+T0N4cBACBRKHAA\nAJJX5bK65mZHt2ytqTzMzzWSw7LNJMhqiDc/lRH//SxAshl5znEmkeQqmOUphc+vkzTxqmM9\n7jfFwQG5FsVBjzpLVXGtpCwfhRUAAKKOAgcAIBmZBpMM87+pZnXL2j1wWOBqxYyX1kmacPmx\nvn5Z7WT58SxCFUvqJXmc0+xqaXPAXklZBRRHYijdshtGMP0g/v91cM1uxKJv5fPKLyWdmP2b\nKL4mAACWKHAAAJKXiUuYwoGstq4qNpGKsKMfoXJ+nrQsjkRdydxGSfkn+RvtUbG0XoE27MZO\nxeJ6SQOjt4kmrVgOo7FkshveY3ddeWQ3imc1SRo0rqf/4qBrvsNkN0yBAwCAOKDAAQBIRq4l\nBsuP/ZZzQCdc7hmn9yWY/Smhsjxnzkj2iSBh/vX0Bkm/+0PnIK8PMrth2eFi9g3t79VMdmL2\nb2rKV9SUr2DBCgAg1ihwAABsIye4X+wHnwJwljmqiuqUiBYS5+fJWGc3DP/ZjZaT+M5umP24\nMR2wSnYjJKYByllECya7YXzw2EZJZ90awj/KQeN6BnNZLJq/AAAIEgUOAIAtObMbVcvr5JC8\nyhP+axYmu2EKHJb8bIqNIvNLb8tFLQnnvWdX0p6fMsoWNES4KqVsYYOkXN/ZlrRtVPHfMxKG\n4LMblr6o+lLSb7M8x2dYTif188eY7AYAID4ocAAAkld4wQrzwdg81z/vFhXzLEebZuc9b969\nRdKUe48K6QzJJirDI7MG9y1b0BClE6U+X9UBDzXlK+SjBBBwMotrA5Sf1/F21q3HBHOZq8/L\nV0hq3uuQNCCvX9gbUoIfFAIAQKgocAAAbKCqqFZSVoHFhyJfHRMhlUW8L45KdsNyVIHrncmZ\n3TAsJ61GmN1oeZFAc0nSMLthBJPd8P5D9XnFioDPCqn84fTbrN98XrHi84oVJw50e+LnFStk\nclMAACQTChwAgOQVUpHCGffw+AQYUuzf+x37jfpGknSUx7yDyqV1cl+xmcyiu/jTDxPxiEod\nJGlZ1q28BcxuGKboYF7TkdEslzEWA4f1qSqurSquDSYo4V28+LxyhaQTs6PTHnJi6+ubekrG\nfs1+Lp7/9mpJo8/vbnFOshsAgJihwAEAsAHL7IYfe37KKF9cH+RQUifLPo5IFqZafgYO+MEY\n8OaMYHj/+fGIV1gKewqG94ube8x5nCw7VryjHwAAxBQFDgCA7ZkwhRwtMzUy8/uVL653PhrG\nyEbLJbIeC1/tkt2Ip6qiuv0OTMAymjhzlhgWf/SVpOFnHB/qK5g/YG0O2OusO/gqewXMbvhZ\nbRNSdiPIWEpN2QpJA3L7O/x2qIw+v3swjTPG8sKVkgZPPCHI6wEA8MXGBY6FCxeOGjWqudki\nITl9+vR33nmnsLBw4sSJ55133rhx49q1axf/EwIAIhHqKo2tjQdrSMvXoWY3Wvd6BM5WBJz7\niJQx783VksZM8eyzCH5MZhTXD0drEcnnlV82/+pQtPe5ZrTx/PuYyW4Uz2pS0CtmAQCIkF0L\nHKtXrx41apTlQ3/5y18eeOAB83VhYWFhYeGVV175/PPPx/F0AIDwWf5G2qN5xOOajLbNm2sP\nCebFnb9/9n+Z93aV2Kkqrv3luzaS8sbYfm6Fxyd5yyCMvXTo+X11ca2vibO+shuLP/xa0vAz\ne2Tst9fyAsufSXibiX0N2Q3Vnp8y2h64t6a01n/hxvnvjkfvibk/vFGmZDcAANFiywLH6tWr\nr732WsuH6uvrH3jggYkTJz733HPdu3c3V77wwgt//OMf+/Tht20AYCfe2Q1fvw/PzO+n/Ije\nK+Bej32nik12o80BeyuW1g1Mj7aX1rxMAgo6Ic1AHTOluyk6eAh1TGZ4lQv/zNLWE0OPdZyY\nHdTo02gx2Y0gN+YCABAh+xU4TGfKn//858LCQu9Hq6urJd1///3du3eX1L179zvuuKOwsLCu\nro4CBwDYguVvpE12wxQ4fF0TjMhj+ZaDSCNhhixULK2L1gtGzs9S3pCEl90IaetN7LSmMPpV\nLa+rWl4X5B8586MbfmbLj878ebOskniLvALi3TvjGlny/0fX2dX1ReWXkn7bWgfxsw/FW7T6\naAAACI/9ChyjRo169913J0+e7OxDcbVu3TpJnTt3dt7TpUsXSfX19d4XAwDsJeAsg2iNPAiv\nt6Jiab1at66EOqojdtmNqFdkAgr4vScku9Hy1oGyG1Hf/ltTvsKxX/Q/+X+/dX//b6oQyw0z\nX14rafxlXbfUHSpJ2REdz4N3diPIZjEAAEJivwLHqlWrTDrD0s033yypU6dOznvMxTfffPNN\nN90Uh+MBAIIUzwENUZwMGs9KQRhK5jZKyj+pVyQvEnl2IxLxyW4E/CMx/Mwe5gvX7EbAwoHr\nj86kOTLaWl+56P2vJY04u4flo5ZdLQHf3bt3xrWCEN4fXWd2Iw7Jms8rv1Tcm2gAAKnEfgUO\nP9WNUDn8rzgDANiNM7vhZ+dlMIWVgGUXy9c32Q3DkdEsr0+kIQVMglzbGZDHx9qoJxS82XrF\nTDA/mZBqczHq2hg03t9eEo83DWYIyPjLutaUr6gpXzH6/P41ZStqylbENF5BdgMAEAv2K3BE\nkfeKWUoeABA33p8PozuLce8vjsqldc3SwKF9vT9yVxXV+s8pOBP7UTlM3DizG1XFtWod8JGq\nSuc2SsoLFFexrEcEU4XxTkyYr80Leiv8x3pJE6/uYm5mFfSrLq2tLq011a4BOf1dm5hcsxve\n4QjXfwucx4iwVhL2XNL/396dR8dVnnu+/+2SzGBITIA4YfDAEEvmDJYleTa2MRCmyJlIME7o\n5JDhOJ2c7turuaf7pO11Vy98Tueca1bf24fcuDPnJMEkIRMKU8DGoyxrqJKzVqwqMdiWjQlO\nCDiBQLBV+/7xVm3v2lPtKpWGLX0/f7Dk0q6qLalK4v3t530eo1a1G51PPSNp8Q0Be4UCazf2\nPfGspCU3DasoCQAwSUzqgAMAMCZGoStE85LGnpC2na3XNpi9A4FiFlnELKzwrEidh42zZWb4\ntRuBUlO84X58/qQgcKZvTN/4+5clfepfLqr6fOLo7chadbKHhnsBo3vnQKreltSyrMH9HXjy\ne4OSbvx4QHlpX89BKVXpEz32jWOSbvnUmWQtf3q4J2/iEhNwePR190tqWjD38W8elc6/+e4Z\nCiqvcNcT0T4DADBujdOAw19J4a+2CNTW1hY4XQUAMP7Vdo5mxF6DOD0moms3yiYU0fsXzCV9\nj+pqLsKub49+7cboTz9x125EP3vYjyM6oAmsmIjYOtT27y996oEjTz1w5IZ1s8wtqfp8U+s1\n5llUuomp5PR8J7D7589Luvb9V0qy4wUcZTt0+Gs3TFRhuUKYTFf//IUm7JAJO2rOXbvx5b97\nRdLn//Udkva0Py9peduVnuOnXvzWSJwGAGBCGqcBR9VWrlzZ3t5+4sQJp8/oiRMnJG3evHlM\nzwsAcEZtazecsgJTGHL6jdTC66rpAVG2dmMUulfEP5mqeVbmXU8PSIr5HfPHBFXP69XI124Y\nnqCn0jaWzrYpJ8wyAYcjsHbDGHqzzqqz0/uyUiGhcI9xDQtKTO2GCTg8YvbKLQ64nR19mKNp\nwVxJN989I1Na4nEgfVDSvOZrPOfpqd2o9OkqNa/lmr7u/r7ufnOeAABEGKcBR8x6Db85c+ZI\nevHFF52A48UXX5R02WWX1ercAACjZjQzBePn/3r8/X93acCZlLZdWLByTveuXPeuXHXjXQML\nCvw1F2FVCe6tIuOnN8Fo1m7Ef3YTe8kOrrkwAU33jgFJC1bFSnmalzQ+8Z2jTwwcnd7wunyD\nS5zaDTerruL/qzG1G0bM17+ndmPXzw5JWvGBK6LuUhpVuB6hTM2IUy2S6eqfNlMnB8+Nc4Z+\npnbD8NduAABQqXEacFStoaFB0saNG++///6ZM2cODg5u3LhRUnNz81ifGgAglLtgodKRrmad\n37s3J1m2XX2PiZZlDT//1+OBn+reOSBZVsqO3ngyzCaOnpNRMeAYUdVVu1QqLKiK2cKjJg1T\nzXaMP754rqRMvt8TB6Sm2L17c+6SmUKFhetJ4/SOcR/jfF3moTw9dMN6rPi/WKfnRcw3hSmm\nMAFHpbZtPSJJqlOxdiP+MxqmwU1tJw1TuwEAiGmiBRxz5sxZv379li1b3J04NmzYYCo7AADj\ngYkw6s7Kq1wrzVgzO3flJFl1VZ6Me439/r+7tGdXrmdXLvB5PelGquL2kWe4typEiN9RYu8j\nz0ladlvU6NAJIOZ8VneU4OQRJiYItGDVHBOxxXTTJ2bcu/Y1PfaOjQ+e7zs9yxOxZTqzB5+4\nSNI1731ZxaaeLz87VdL1dwaUe9SKu3YjuozF7+a7oxrQHOg9mKrTvJZrNGJDcMPs+cVzkpa/\nb4K/zgEAVZtoAYekL33pSytXrnzggQfa29vb2trWrVu3du3asT4pAEAU95Xz+LUbbvnTlrlj\nz55cz55c6/KGmIvhOGKeUkTtxsNfPi5pzecDNr9MBmFBVcwWHjVpmGpaaZplv5ne6q7F8Lc7\n8WcB7uNnXPWmJOl8+TiHFaeWFLZ7FEaZdIfmLIUz8X2x7o0kv/jfL0h639+O1Mbb11+ZEuew\n/Gkrs78/cJBKy7LGEW2Z8bZL/3wgfTB+dQkAYFKxqu52MSFZFt8QABglv/zOUUnv/cSMQkHH\nlLzKDVIJyyzcu1rObOuwZQ6Os8EhsNuFfwNFRGgSMVxDkz7gGE3RP4jCMZUMKvY84AP3npD0\n1pspSZ/8x4sj7jj8LUv+l+VIBxwxRU+KdUbPVv34zrfuB196SdId//VdzqfcrU8BAPCYgBUc\nAIBJpXvXgKwzAzjNarBnT06WN7Bw4gkTedhDlmpU4lHWiEYbIzdyJUHu/8Krkr5w/wX+T3ma\nX8gVbYQlHWXLf5xoIyJPOf3n6ncxuduvuDc0+aONOIFORU9q3jXRDxsYbez48SFJqz58xcvP\nTJVpdBKSgERLd2Yj9n8N/Tklaf9TzyxyjZsFAMAg4AAAjBGrUDEXZwNI144BSQtXlSw4i10V\nAtpvvHrkXElaLpXWblgp9e7NOTMibNvq3jWwYMWcwG4X/g0UESvemqwwYzbdNPzr9kTr3jUg\nFYKqso0q3RUEZvKrVIiQrJSdP5Xq3ZMbzghbN89Pdt3G6dU9TnTVg9uT3x18xxVSMWswAYdb\nDbdfVaevp19SU2v1NRrbf3BY0uo7Zgd+Np+3TOrkrt0AAKAsAg4AwPhS6eLNqd1w3/eGj830\nH9l6bUOh2KEYCphF9egLm/9aHXftRtfTAyrORol5bb/SoGT8FIyY/rL1UyWp6abfS8rsf9F/\nmPnSAie5hO1Sif/y8397nW97RQGEp+/sK4fOvfGuwms4rBlt8QdRWcIV8eNLucbZVhHYrfpw\noa3p9UGzcuMwGVDz4qjcpCZJIgBgoiLgAACMHmdx1bM7d+EVar22IXSAaOkybOGqgCqP6Ivh\nJuyoO8uevyi0l6Q7HFHIONLq1vMx2xB4MoiKig4mTO2G4f5ZRCzai3HMXBUzi6bWayTtaX/+\nzN1rlBzViinMeWVw6g3rZinGK8qJNgKl92VT9Wpe0hhz8ku6M2sPSa7vqlVne3qsBtr3xLOS\nltx0tf9TTa1zf7HlhWM9L7xvfZXdQMJqN8wJm3Mzk27d42AAAIhGwAEAGD12vuSfPbtzsgrd\nQFWMJPw6n3pGUv05+VqVPIw5M+SlZ3cuVeu/w6Z2w4h5rbvSoCRm1lPRdpvqmFzM1D4sbys3\nczfGyGFJ3TsGJC0oDdQCq4rMkfXnDnmCtupKDMrODParroimZVlD2rfnxRjz4oj5i+aGnVtZ\nu356SNKKD5KGAMCkRsABABgDZq1YWDeWrjzLFvZH7GFx3xjxOO59HI70vqxlqXlJo2dlXt0y\nMrB2w12vYaaWBi4px7zDwnjmiWMynVnblpWyw6aWeng2B/XuzZnhacPPzsJ2+mQ6s6l62bYu\nnP1GuiPbvLSx6t09hZaoFcYQ/kqNmONjzn7b6VcHz93+4JHVawO2nFRduxHNfW7Dqd147BvH\nJN3yqctrcE4AgOQg4AAAjJK+nv76cwuNCc1SzbIs+XoW+Jf3i8uNS8jsz0pyb0Xx+O7/9VtJ\nd/33d5qq/rqzC0UjEc0sR66FpwlQnEanI8TZbhOY5oyC6mo3fnzfbyR9+D+/2/zT3Zizd0/O\nNKZtWdZY9qfjnhxcEU/tRvFxChU37pflgqBtUzFVNKdW5qdpW5Jl1SV4mH2cgc1VoHYDAOAg\n4AAAjIae3TkpVX9uyR6VFtdAyt69ubAr256r7tGlDf6lb8+u3Nzr1b/twph3KZxVR5Wl8hHM\ntfdChUj4ZXznCyy7DK6oWan5ivJ5K/5dxqfM/n4nH3J2duz62aFdPztU9pq/5ws3zSni7F4p\nmywEpi3pfVk7b6n0x11M98pXnfi3+bxy6Nxth45cf2dASUXMZjExu8PIfEVLyx4VpWzyGJP5\nFWGlbEmpelvSvJZrou9C7QYATE4EHACAkeXurOHMlXQv2j3tEsPyi8e/dVTSzX8zw/+p+Ysa\nzQV2SamAobGSdNd/f6d8yz9TuxGYZYxcC8+6s/LlD4qh0MEkhHOdfPRrN6qzbesRSRdcouvX\nzXr8m8ckXfye16WU83poWd5QfJ3YzUsbn3rgyFOHj1x4xRsK2rVRUe1G984BK2UHvvCqqAEZ\nCc5Pc9vgEc+n4uQa8UONaM5zmR/QzXdXFiI4X0X7/3dcUtu/v3SY52NQuwEAcBBwAABGg7mO\nHcYsz8LGmpqr7o8/czTOEznBhLO7Ifr6fM/unGQ5i9ue3Tlzqql6u6JeCZ6L7WHTYSpSfguD\nXaaexb3fZ1xNXfnhP78k6aP/5V3+T/Xszl38Hv3umfPC7utUPVx01Rsqdq6ttF+DeXm0rphr\nNqGUVd3GiuB+GbYkzY8chlp4Ul+hjbt2w7xfnGKWiJfr6TdTzsfRMYd/g9jI7dUK5BmXa9Sk\n++nj3zwq6ea7AxJSAMCEQcABAKgl/3LIFJa7A47AJppdOwakVP3ZodUNgbUbxhPfPipNvemT\nM3o7sr0d2fhrUbPFowrDyS/yQ7VpvzExupCaF8zQqdTbLy2US5iXx813X+4fqOHeJ3LDullm\nB8QwV7+eGo29jzwn6ZwLTit2f9m+rn5JTQvj1kc0L2ns3jHQvWNgOF08HOYkTTcZ8y5zXhhx\nCjecWbA/+B8vSbpqefnnUrnaDXfnlEC1qt0AAMCDgAMAMCKciganhac7fbAs9e7J+S9Q13BQ\nZdj6qrCTpd4slS1ZJX0ZWq9tKNsjI5Dna4nIPn6bO09S2qqszWR1UyHGbQgSWLthRJyzZZU0\nwvA0d6hoMG2ckStx9O7J1Z0VdcCjXzsm6dbP1LIlRLFxbJkXz++emSpJC2K9DN6z8hVJsrzv\nwTi1G2Fzf9w/oD3tz0ta3nZlxONkuvqV0vzYUVFFqN0AgMmAgAMAUEthy6HevTnJMlMwTh47\nR9IFM940jUXNHoG6+orHQ5hrznf8w7tu+mRh6eKp3Sg7b9VZMDvtPLt2DEjWwnKX1t35RUQT\nBLOJYOitlKQFK+eYkMLdJSTT1S8FrOicISBxRsY+9f1BSTd8bKaKX3LdFFu+Ov9xyFPv8+R3\nByWdM+30tWuulApbOdxqlUpEWHbbVc5WqbI9XE0hz9BbqUpHxgTWbsTsEhrIxIimT42uLX+8\nea5Xj5xz/bqryx5sviEmE2xaMDdsK5ljFH5MAAAEIuAAANSY2es+fW5AYFESQFiyUnZ6X1Y6\n0yCgd0/ObGkJDEoCZ3+GLbe6nh6QrJQvN3GHBaawP76IsbLxOXGMigFHmPS+rNNhIU7thruf\n6+jr3jUgacGKkp+Of9H+9A8PT5vxphQ6KPecaafTHdkqmj5UN5i2rExX/9CbKYVX5QQ+b/eO\nAUkLVs2ptHbjiW8fdb9CDM88nbDGsZ5tXDesO9OwwylvKb7mvd/94XTZiFMhEl27YQTWbux+\n+HlJhcyrcs7vh7GalwwAGE0EHACAkWLbcvakOEvczP7+C6/U/EVzezuyti3LOhNY9FbYEeOO\nf3iXpMz+30ccY+KS0DMsdgbJnyp8ULZ2wy/ikrsndvGHFGHV+OZ7kt6XTdXbYZfKnXaMpnbD\nEbHajFMPMqpSslJ2pqvffB9uvGumu+VnDfcrVcR5XlO74Q+hejuyklqWNlbUhKVsgUbLsoYn\nvh3QSffJ7w5KUy96z5/iP1e0/FBK0puv1l+/LmDcrJ9300qMn4v/iy3bmMOv2IQlYAtQ2SoS\nAMDkRMABAKgxZ697b9D41UCPfPUFSdMbLEkLQq7D93ZkU1MqGGax8Lo58Qs0Yq75h1m7UdYj\nXz0m6bbPFnIQZ/0WM5gYzeTCX8ziqd0w/Ov56z462/1PT3ww+mNZy3birLQlhFnbL1hV8oWn\nO7KhJSsu/toNR/x2LYGcMpM/vnjOedPfUlU1EWE7qkZU1bUbxmu/Odt8QO0GAEwGBBwAgJHi\nhBHu9blzCbdlaWNxzuWwmAd0LqqXnEC5PCIsEaio0sGzfaDSA0yjB6uw+A2djepXRYuNihKQ\nSss9An8EKh386d9kNMqr5Ziiqy3ipGyZzqznB2RZtlW4SZ7nAAAgAElEQVRnZ/b3V9qi4sa7\nZoZ9qrq2Ha+fOGvazDcquktFMp3ZVJ339Rn4VQcOhXV43jLuKIraDQBAIAIOAMCIKOw3Sdlh\nq8F0Z1ZWYQ3z7r94TZJdOiLWs8B2HiesOt0+bfXsyqWmhO7pGP+c2g2PcbSppMgTHqX3ZS1L\nth0rFunr7o+eXTqaan4mLcsaMr4Bt7ZtSbL8fVPjCXzNmxanVp3duzfbsqzRhEeHOqdFTKiR\ntOr22dGdXyKYNCpZ20NWfPAK88HP7z8u6f1fYEItAExkBBwAgJHVsyfnGcXqZy5Bm7Vxz+5c\nFev5lqWNZr0XfAKRszDkqyxwn0BgZ1O56jLK7h2IPiD6xPwe/vLxy+b/QfHqCIajyp+Cr9Gp\nuT7/wKYTkhquq3KFPwrc1RDVjTJxeEoSMp1Z0+x2nIwXqbpqJmbJVZzaogO9ByXNX3xN/Gcf\nP6EYAGDcIuAAAIyIluUN3TsHNGRZdbZU6DZqwoLUlHzLUm8u4F8Yl447ObP4DLx0nN6XTU3x\nXeI2j+lqfeBUuYe1PIxZ89+zJxc4oiWQGSvjtCYpfboznSzKbnXJ7DfLy7fHedI4frHlBUnv\nW39Z2SPLdog805gzRiyS0JWqM4ikhkcaJoOzhyyFdCEJfM07LU7Tndl0Z3bBysZMZ/Y9K1+R\nAio44v+sy2pe0tjbkXX6B9dQ51PPSFp8w3tq+7AGtRsAMBkQcAAAaiN0O31kCNBtyuyLg2Jj\nVg1UNK41TolERG/LurPzYZ9yhxFlk5H0vmzgMjU/ZHXvyi0onccRsQvg8vl/GM5Ez5oIzGL2\nPvqcpGW3XhV4l3UbpkuSpo/0uVWtiqqNmG1KquiWEod/7qn7iUaiIagJKx1lI7kI81oqqN2o\nrV0/PSTX1hUAwERi2fb4LRYdfZbFNwQAqhTdLzBs/e8EHIWRnJEPIqe1h2UrRsDhboXgPGbY\nmUQkFHHWip67exIK9z891RDmOxAz4Eh3ZE03B8959nX1S2qq5AzLihgv4l/ZmhN+45UpYQHH\nBGNqkcwQYk/AUfUsXnPHgR3vWLcxVgzkDzjcRmHiyXACjjFEwAEAExgVHACA2nCnEk7bi7DJ\nGkZ6X7ZuimIGy4VpI5KklmWNvXuzvXuzZvhIRRUNxzJvk9SyLP49gjkLSBMceL6K/FDJTNCI\njoyeaMNEFfnTKf+R3btykpWqC36cobdSvXtyYdsizEnWStiaNizdiH4ZJEi6IyupeWljqs6W\n1P3jd67/nxfW9ikaVr/Su/eV8pukdufqpkQdcOpPwS8Uk69Jmr9obl/PQUmnXq9XVQN6A18G\nXdufkbRwddxtJqMfN6z44BWPff3YY18/dsung3v6AgCSi4ADADAscdauvR3ZsGqL/GnLXffu\nyQX8rHhtL4ywSpDL5/+xd29OVsmEl8AlZbGZwtzePbnePblXBs+RdMO6WRFPWqgxiTiryE6T\nQ6dLvgNOi9N0Z9bEO57zLO7Wmet53q2bTki6c0NJLUBFuzACazfCOra6E5xkTdmoud/mzpOk\na0tujPM9MUUfThTlqfTxPIJVZytfeKkMp5Li9Bt1Vsq2nYfal5X06uC5klbfEfU6D3PW205X\ndPz57/6z/8YdPz4sadWHZ1dxAmFMkvLGH8z/+pb5PQMASCgCDgBA7TkL4Jalje7JCwd6Dkqa\n11rYft+8pLFnd86Kt9bwLKr9iUnv3uzQn+tUvHrsXGx3H5Pel53RImc5V7We3TkpZVakLcsa\n0vuyVunytdLZKCquLQslKtc2FG9JqZgiOQvabVuPSLr+zpL1Z3RLy2GOBXGkUrakR776gqTb\nPuvtWGnO0/8DTXrthpM1OC8n890eOjXQ9fTvnE0ihR+Tzh/Oc5X9SZ3ZeJWy6846k/d5Yo6F\nq+Zk9vdn9vd7AjXnn70dhVeXfOUb0y5/0/OkD23+jaTb73l3BV9JuAPpg5LmNQ+rDceunx2S\ntOIDV0ja+8hzkqZedEqlsWZYeQi1GwAwURFwAACGZZhr19ZrG9wJSK3W4YZdnN7iud1EEnEe\nwUkNouMDd7TRsrzBTLXIn7JUVS+GEuUKVsJKY86/8NSwnncYJm3thhG4eI7/PXHCFE8w4YmN\nWpY29vUUNpuYUCPdGfqSjiiz8rw+zXkOZ0OTKfzpeOxZSUtvubrs8YHfmdrWbhg03QCAyYCe\nmiVoMgoAI8Q9INbcYloq1p+TV4y5oZUW4ZtVotnwEriuS+/Lmqmc8Wd5RujeMSBpwao57rN1\nBxxlx6wqqAlob0c2/1bK/cjjUNimlckmuuWnEedlnNnfP/RWSr7owem/69/tsuNHhyWt+shs\nz8FHe98uac3nLx39HijxA46yTI7T1Br13tnT/ryk5W1XDv/pAACJRgUHAKD2PAu5dLEY/qWD\n5z968NitNaoPL7tcbFna+Pg3j/baOUmpOttdu54firkzJvIEQhor1HCuROqs/Fuvhf6xrqL5\nQrGrSGHx/NT3ByXd8LGZEXd54ttHJd30yRnxn2Uy86QJnm+4I2xg0PxFc0325729eGTzksa+\nrv6+rv7ooTmS1nz+UvPB6G8RckcbY9WTJXAjzI6HDktadfvsUT4ZAMDoIOAAAIyGuil5p0DO\nLHharw1d8HhWRD17cpJVUY1AdK2EYQd1iyg8466cpNYVcZ/xTO1G8cw9JRvzFxXalEYUjJja\njUe/dkzSrZ+5XFLL0sZ9v3w25jlUIbO//6Kr9fKz5w3nQSZ57YZp9TL0Vqr+3HzZg/1RlHec\ncEeZt0bJfYvxlrt2w6i6GnUcNoiNrt0w6s8ZMh94eoUcz7z9eObYLZ8KTVR3//x5Sde+n9IP\nAJggCDgAALXnWcg1L200l7Xf/RevSYVGmpmu/vnFS9DVjYFwH+9cNves0G6+e4bZ+jF/caN7\nM0Xr8gZze3pfNn/KSp1lV3MCsdeB6X1Zq052uRkxfkve67oS7mubWkWpiD9hiS7fUFDtxnDG\ndkx47nKJx795TDrv5rsDFtimIsO8XH/4zy999L+8S9JvDp4vSUvPHOZJysw/Cy+kVMABRvOS\nxu0PHtl+5MgFs99Q5T+p9q+8IKntc5epFpFH2H37uvtVuj3NzEguW5nit/jGgKm085qvOZ45\nVnjk4j6XVbfP3vmTQzt/cmjlh67o6+5/26X64/GzK306AMC4RcABABh3PNefI2oE/H0rAg9w\nS+/LmhWXuZe722j3roEFK+akO7Kp+pIcoWwLAyd6cNZyZsHp2YYQp9/HrZ+5vHdvrndvLrrf\n6hPfOSrppk8Ma9tI/nRhiIY7sNj+g8OSVt8xeziPPHk0L23cv+0ZSfabqYWlrVJ692bf2aDf\n5kqGquz86SFJK4sNL5uXNP5iywtTp1U2WrVw38WNKgYcVQhMLsw/X+h7oaJ7ufX1HJTU1Dqs\nCSmOSruHeOa8+Gs3+noOTpupk4PnOrdQvgEAEwkBBwBg9IStizxXmO0hy6qzzQAUTwuDsAWP\nc0v+lJWaYjspRuEB85LUutw7PKWwI2BFg6TuXQNVf11GwMYWqxDVeL7w3r1ZhQxA6Xp6oP6c\ngA0G6c6sUmNfNGGusTcvrvga+wS26PpC+YBJOpx/GoHlG2H87Wn8Q14z+/utOlu2lenMzl/c\n6D7AXeOzeu0shfDMW/nhP78kyZSQGKZ2w4iovzBNfKt+TfpbC0fUbvjLPSp7rtJ9Lis/dIX/\n0X7+v45Lev9/uLS6pwAAjAcMDSnBFBUAqAnPZd7AcoOIRb6KQYZsy8xJbVneYJIOpWyVu6Lr\nbeGxOyffTArzaFbKVummj2iFe9XZClr4uQMOc2RqSl5BDUEiVmtdTw+YyTKexy8sSvOq6IQ9\n4gxzKXt3y6pmE8FkEBhwVCdidEihZMO2VHwpOi8kE3Dk85aC6p6crNAp2DHHm60uJRu+SlPF\nwEE5ww84CuccVBLiv3GYAYfpZfPmyXpJ131ktucBn/r+4J9fT51+MyUCDgBIOCo4AABjw9kf\noaC6jJaljYXtJ8sbVJjDaqXq7bBow70iMv91VnGeaKPkHPLle5c++d1BSTfeVaZRhaTWFQ3p\nzmy6MytbpuNGydX1crX95oCF14UWuXjKT6LFaZ1QXd5BuhGm0mjDTJb97cB5km7728vKHV7g\n/nn1dffLsvt6DpotISb5MnlEhMBIwmynGjplKaT57ratRyRdf+csFd5cVhXRRvx3k0fV0UZ8\n9efkb/tsbQY8AQDGCgEHAKD2POvqmK0ivAM1LW9JXdkeFoGjN7t3DkhasLKkP0LL8oay60Dj\nwivf8JyAqdQoy7Otxr1ojF6tuTtiuJuMxGn0GJ1rVF27UZO7T2Zlm8V4xBkd0rMrJ6Xqpw55\nbvdkds7byv/2cZcCZVz7VjxHmgc0AUfNBb5Waz7Gxd2s13C/B8v22QUAJAUBBwBgbCxYOad3\nb84s5k1dRm944rCgtH2jw4QUliXJalnW4F6kRV9e7u3IWilZUrojG7bjw9RiFIa+uESMj414\n0rJrttou6srUbhSSIAKLUVNI6w70HpQ0r+UaSQuvmyNJ16mvp7+v5w9xQg2/N14+a8lN3tV7\nfDt/ckjSyg9dMb9cOYap3TD86VtMTu1Gpqtf0vwRqwZ6+keHVdyNYtz/+VclfeHLF4zQMwIA\nxgMCDgDAKPFMOf3lvw1K514850+Fz+7LWnUly/L4oxMcgW2UPLUblbJSdvOSRlO1YaVcjQmK\nHTe6d+Uk1Z8d3G5D0eNXguatOv/0lrTEUPNL3xiOn/zPFyV96D9dUsV9A3vHuJmgrWd3zinb\n6Xj0OUlLb73KfVjY66e446nMkNTAAqjq7Gl/XtLytnE6tcS9BwcAkFAEHACAEeR0eejdmzV7\nNFxlGueqwg6F7qu+5uqxuxq/6+kBKWWuipftLlGoGfEPkTU1ILbk6uVhOg5En5udL3OAI3AQ\njNNytYbryQhlL9ejhvY++pw0ZdmtV6lYu+Hhrt3wlDaYsTWevifOyzvTma2bEpzrxWTmicTn\nrkCpqHbDo9LajUorPty1G6af6Be+PLevu7+v+0WzOeUbf/+ypE/9y0UVnQYAYJwj4AAAjBLb\nVsuyRifguPDKN9zxRNnSg969OSll5pJ4mGvd/gzCDJp1/1OeVqaxV2jubSkmBGldUXicBSsa\nVLwe3rs3537M8teEbdl5y5+zFE6vktoNjEOmdmPvo8+VPdIZ3WoVe++a2g0TcBh9PQclmX6i\nbs57Z+mtV/3gSy/94Fcv3fFf36VyYhb7OFmb2daUmlLy2ae+P6jYPSyGX7ux5xfPT73wLcUb\nJNT55LOSFt8Yun+n6QO/7e347e+fnyrpxo/PfMfsN8KOBAAkBQEHAGAEOTUUzjjYQp9OV7sN\nf1/MiE6ZziVckyMURmYqJaejgSRp6K2UmaDp6Nmds+rKn7C/oiS6xsTZR9C8pDEsp/BwxzDO\nvZzvzwjVbnj2B2E0mdqNb33xd/Pe9zuF/xRe/NX5ki7569eaFzc6NRqBM2vmL5r79A8PP33k\n8HUfjfsDjTkxp+zwncAKFO9zBfX6rZQnjjRv/D2/eL6KhzIlG7t+ekg6Z8UHCxUrn/qXi3o7\nfjucMwQAjEMEHACA0eN0JTS1G+nOCuaeOpUR239wRNLqO86URZhr3Z4lnNkzkunMOgste8hy\nOhqYhKXsjFi/wK4ZnjM0Mvv7L7yyzJIy3Zm16irbp4NxLrB3RmZ/f9P7ZQ9Zp96o27/tGf9A\n2ebFjY/86pikFw+8TYsDHjbdmZVSZV8qcWo3qhMYWIzy/JHl76ugBiSidsMopCdLC/+sOyug\nOgwAkCwEHACAMWCSDlNS4b9cHLN4PtOZlazAdVch8nAFKBHNGqsYBhHnYcP4L+C7U5gRQu3G\neNC8tHH/tmf8t3fvGpB022fnPPK/XzC3zF80N70vm96XDXsvXPfR2ZU9t12mR4zZTvWOCh/V\nr2wNSExV9BiO5tRuhIlTnAIAGOcIOAAAo8eqs2WbJZClGB1GnUkl7htX3zGrtyPb25FNpUoO\nzg95l3BOauAvuzC1Gz+//7iky+dX8CW4H8Q/5cTpFSopfyrlu3fAo2XCy1gKG3AsKV57Rfez\nD8dIj/Cc2AIzr/mL5qY7sr17cvVn69ndFyy6Pvi+t/3tZWEP63mzPPq1Y++a+7rGrlFL4Htz\nItn7yHOSlt12VdkjAQDjBwEHAGAMVFcu4dmWnz8dfFE6cEyJpKe+Nyjpho97i+rd3Tr+6eN/\nlPTF773NucVzRTpmL4P4GGiCBSsCGq8Uhvjsy9pDloYdZHheZv4tWjHHozqv/1ePnuv5lOed\nMsxSjoi9YMPkHgQz0s8FABhlBBwAgNFT6RLCf33YktId2ZaljenOrFK2+wE9iYZ7fdW8uDHd\nmb3w6j/9/tmpu352SNKKD1zRszt32Ty1XtuQ7vyDpHRH9pUj50oXxjmxvu7+pgVz/WtOd/WE\n/7P+JV/gEFBHpTHK8Gs3Cs9L7cYIaF5aaB16xz/UoE3GrZ+5/OkfHXbfMpydVmMr+l3g8cS3\njkq66W9mKCiqKKu3I1t/doxT6u4/b3qhOykAIEEIOAAA4465oPrSr8+XdMunLjcrt7op+Zal\nc800kGgm6TBpguPEwfMk3Xz3DBNwZDqzdVM0dMrK7O+3LNnF7S2Lbvu9+1K2f/jI/EVz+7r7\nFY+z16M47cVSJZeLd/7kkKSVHyrTOwBJUUXhz/CbWZiXq2et7q7dqGzoiSVJfT39FzeoqbXw\nmIWXdOmpDvPMR66e4vSfU/Wl/USp3QCACYOAAwAwxuJv+kjvy8qSbau3I5ty7Ssxj2BZkus6\ncNj6asUHrlBxUdd6bYO5b3PpPIVoZq3ob8ARyB6y0vuyVurMKblnx8S5am26P15/56ywrTeY\nSGK+riRd95HZ7kako1O7Yap7+nriZnxxDJ1KpabkM139cUqHTO2GMa/lmsz+/sz+/vjJUcvS\nxgM9B83Hu3/+vKRr3x8wmYXaDQBIKAIOAMC4U7igWhyWaaVsSU0L55qiDBNk+Jc0ti1JD3/5\nuKQ1n7/U89mb757h/qdzvTpsaVTYThI7TQjsODC/eM7uZ4l/udjUbpiAw6+iwn6Mf11PD8hS\nne9/ze771ElJ//kb06p72MC1ujtVrKILjFO7YTgv6dGpOaqiD477LvNag7e0uEtdqtj8AgAY\nDwg4AABjLOZCJdOZtSzNX9zo2XuiYrfRVL3tud2sWIbeSrUsayjsczl7SFJTyArH0bs3J1km\nWPHzN2iMUHWhvrPccrbMULsxwXTvHJC0YGWhw+jQqVTdWfmh09bCVQE9RwPFf3UFhgLD7QNa\n4d0L71xbKkaHppqpit4xLz839annjtywblb0b4/tPzgsafUds6MfzeQyJ49Pk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"text/plain": [ "plot without title" ] }, "metadata": { "image/png": { "height": 720, "width": 720 } }, "output_type": "display_data" } ], "source": [ "FeaturePlot(obj.integrated, \"isg_score_small1\")\n" ] }, { "cell_type": "markdown", "id": "621a6bc2", "metadata": {}, "source": [ "\n" ] }, { "cell_type": "code", "execution_count": 24, "id": "048239cd", "metadata": { "lines_to_next_cell": 0 }, "outputs": [], "source": [ "obj.integrated$cond_tp = paste(obj.integrated$condition, obj.integrated$tp, sep=\" \")" ] }, { "cell_type": "markdown", "id": "1336bfd6", "metadata": {}, "source": [ "\n" ] }, { "cell_type": "code", "execution_count": 25, "id": "23265937", "metadata": { "fig.height": 15, "fig.width": 30, "lines_to_next_cell": 0 }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[1] \"limits -1\" \"limits 1\" \n", "[1] \"7\"\n", "[1] 1\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "\u001b[1m\u001b[22mScale for \u001b[32mcolour\u001b[39m is already present.\n", "Adding another scale for \u001b[32mcolour\u001b[39m, which will replace the existing scale.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[1] 2\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "\u001b[1m\u001b[22mScale for \u001b[32mcolour\u001b[39m is already present.\n", "Adding another scale for \u001b[32mcolour\u001b[39m, which will replace the existing scale.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[1] 3\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "\u001b[1m\u001b[22mScale for \u001b[32mcolour\u001b[39m is already present.\n", "Adding another scale for \u001b[32mcolour\u001b[39m, which will replace the existing scale.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[1] 4\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "\u001b[1m\u001b[22mScale for \u001b[32mcolour\u001b[39m is already present.\n", "Adding another scale for \u001b[32mcolour\u001b[39m, which will replace the existing scale.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[1] 5\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "\u001b[1m\u001b[22mScale for \u001b[32mcolour\u001b[39m is already present.\n", "Adding another scale for \u001b[32mcolour\u001b[39m, which will replace the existing scale.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[1] 6\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "\u001b[1m\u001b[22mScale for \u001b[32mcolour\u001b[39m is already present.\n", "Adding another scale for \u001b[32mcolour\u001b[39m, which will replace the existing scale.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[1] 7\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "\u001b[1m\u001b[22mScale for \u001b[32mcolour\u001b[39m is already present.\n", "Adding another scale for \u001b[32mcolour\u001b[39m, which will replace the existing scale.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[1] \"ncol 4\"\n", "[1] \"split_featureplot_isg 30 15\"\n", "[1] \"Saving to file split_featureplot_isg.png\"\n", "[1] \"Saving to file split_featureplot_isg.pdf\"\n", "[1] \"Saving to file split_featureplot_isg.svg\"\n", "[1] \"list case\"\n", "[1] \"Saving to file split_featureplot_isg.1.data\"\n", "[1] \"Saving to file split_featureplot_isg.2.data\"\n", "[1] \"multi list case\"\n", "[1] \"Saving to file split_featureplot_isg.2.1.data data.frame\"\n", "[1] \"Saving to file split_featureplot_isg.2.2.data data.frame\"\n", "[1] \"Saving to file split_featureplot_isg.2.3.data data.frame\"\n", "[1] \"Saving to file split_featureplot_isg.2.4.data data.frame\"\n", "[1] \"Saving to file split_featureplot_isg.2.5.data data.frame\"\n", "[1] \"Saving to file split_featureplot_isg.2.6.data data.frame\"\n", "[1] \"Saving to file split_featureplot_isg.2.7.data data.frame\"\n" ] }, { "data": { "image/png": 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X9Mw01U6ZuToAcyVJi/72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\ndNONw8anvjJ+YFS1C3oSGIU/qHZ8x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Zdccom7U4ywcNSiWh4ERgujD4xap0XJtS884G8igDfYq2u3buIvna+z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abgWXW28oGdzKgwoowP1ny8n4gMM6PsxYeI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\nD2FpUSIqKMiPRCLsDP9IJHLy5EmKpkWJaHB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YUJcoXUULolRT/7if2Fv/b/hJC5mH337t3awbuN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\n9vrTpUVh9ARGR62ou5ey18PaXqfMXTBzLZNx15oWDSX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CXVnYLC0a\nDf1NU040nxaNqN6PPqVFY0dOtCQtGlFNiz7/0G7SojGzGE45UWlRAIAeytKi2djSNz5SGzNZ\nbHTFilqH0ayPfuP96PPNShtvAxWtup/m3fmNZdnYkcKJ1jVr1mQjAAAA/bDQr6xybRnAvNiy\nZcuuu+7ak01ladFFS2rTi1s3R0RMrivoG1p4JOPj4+nLkZGRkn3VTV9mHyB1DUobW5P2RLbZ\nx+4dP1zfyy0z3FQ7g8y7AzBnsrTo4c+rLmRp0UcfUdxvviP59ktZWvQ3D6wutPnLfnx8vLwc\nLXHV6+LU98545HvXxhEnREQ8/9DqI/94U/2r/uIp8ZFvdbdDaItqZ5B5dwAGSkd3FvrGR+I3\nnzQeESMjIx84I159efzN6fGqyyIisrRo1me0mVTBZmnRRYsWTU7WuoRWKnHLLdUSt2Wr0a9d\nGn/00nYP/vor46jTItuLDqP0j2oHAAAK6TAKwDzoVVo0IhYtqY35nqCfu6h2DX2z16Zep1u2\nbEkn5luens9vPz/RVLej/ArttxptKW1QWhQAoAspJ5qlRSPivj3GIuLRR9Qeqbv4p3117Zd+\n4zHVsbFuLJEuYcouZKpTflRXva42Jt+7tjYe/JiIJmnRbGzHW54dEfGu51e/fOHh7b4QAIC8\nxradzW5qlLzjxPpH0v3o02Tmqy6rpUWfdEA1J3rPTSs2/Kz1MYyNjS1atCh2pEUjIo3pSFau\nHI32Wo3m06IffV3z9SKuv7I2RpNSOUuL3nfffWXbAgAAoCsL/coq15YBDIErz47TLpzxyGfe\nXV0ov9V7pRKbN/es12nh9pOWHzUtL9OPDtsMQEa1M8i8OwDzJd9SNL/cdav47B6gs9Gsw2g7\nR9Wsw+h5z6l++bZ/KnhV+x1GU1o0InbfLSJi4ofVL//+xrZezoKl2hlk3h2AeTE2NrZy5WjW\nvDPTOPU3MTGxYsWKLC36pk+02PKTDqguvO+C6sLoH8feDy07kpGRkUouplrXYTR/e6Xs2qry\nj44sLfqiXGl6/gnx5mtrX+Y7jJbI0qIPetCDWq8NRVQ7AABQaKEXyv5UANjZXXl2deG0C2dM\nWX7m3a3ToklfPwfaSXm2cwvUuTlahpJqZ5B5dwDmUf6Knfxy+1fppFP4MbMXVOFrt27duuuu\nuzTb7J8fGh9vaP9Zd5zlR1VYT2YvOe85xWnRlp65PD63pvblW54dq06Kr/xTPO05cfhz44WH\nS4vSmmpnkHl3AOZFm7N8+TvLv+PEeNabW19tHhFPOiC+89OIiG/9fYu0aERknwKVJq1N60rQ\nNo/8o6+Lky+YSo1LI+L8E6qPv/najq+Hv++++6RFmQ3VDgAAFFrohbI/FQCGQOow2nLKMpsw\nzTTOUfaji2c73UN1GKV/VDuDzLsDMGd6XkrlT+FHaYfRlBZNy40r/Pmh1YXCzGizK4sav526\nerKwNu4oF/vM6o1AZ2RGI+LGT8Xhzy17IeSpdgaZdwdgXmy8O/Z4cFulaXZ5UqoJV64cLXnV\njdfG4Sd0XPROT0+XpEV3rDPjwZLtVyqxZs3kwQcfnL7MZ0ZTWrRxg9BXqh0AACi00AtlfyoA\nDJOSKcvslkx1mdG6lyedfjK89A/i0n8vfqqd7qHQV6qdQebdAZgbfTo5nZ3Cr9tX415m2WE0\nLeSznkn5t1N3JPnttLmFfIfRdi5wgkaqnUHm3QGYexvvri4s3a+zF5bXbzdeGxFxxIll62Tb\nadHZ9Cux4mm1lX+xLiJi/8fNWGdqe3zuojju9cVHmDKjWVo02bx5c0TsvvtudXs/97h452fL\njgdmQ7UDAACFFrVeBQC6lTVeymty1XpnfvWjgs2WTP58986I0rRo7JhL7SItmo2N0ml1J9cB\nAObLNz8W//7RiB1lXpabLHTTZ9ra5pVnR+zoLZqXCt3GcneXXZqmRSPXW3TFg2c8vuuiiKJ6\nsmXV+pdPjc9fVL9Cfjtt1r2fWxMvfnLEjn+0kn+6nlT4AABDL+VEO02Lxo7K7fiReNZo/KTh\nWqPDT6itU54WjdLKbeIrtTHbVGFaNCI++57qptIMcLb3Gy5dVpcWjYjddtut8djOPa42AgAA\nMGcERgHolzRXmMbs7HI2L/n1D9fWbHaZ76rlhQ9X06L5zGjddOcvb52xfno8ZUbLdXG9ceot\n2qzDaEiLAgDMk2eNRET8/ikRM9OiWWl6zz335NdPadE0fucfm55KT2nRNCZ1DZ+6a2GT0qJZ\nZjSlRfOZ0bxsF1Pb09e1p/7yqRERn/9sfP6i+l2Mjo6uW7eubgslUlr0xU+O0dHRlStHG1uc\n5r+UGQUAaNR4yU0XadEvvC9iR/32tx+LiBaZ0UJvelace3yLdVJv0azDaETctbn++Bctjmee\nFRFx/DkRUZ8Z/chrIqI6xsxvP2VG81JvUR1GAQAA5thCb8XvZgQAfZVu01l3G81KJb52aXWF\no19SS4tWcieZK5V45rLq8uo10ehXP4rfOHBGV9FsOUuL/uZja+tXKnHUo+Ibt3Vw8Je8Is78\nYAfrF/qHc+MF7+z4VRs3bly6dOls9w0RodoZbN4dgP5JadGI+Mz4jMezW6tnadF99903e/am\nz8Shz4rv/GM8+aTqI4W/p688O067sLrcw/vdr3hwTPy69uWui2LL1IxjrlNNi0ZUWzhVIiK2\nb9++ZMniz10Yzzirfv0sLXrwwQe3eUgvfnJc8e0Z32Ph99vy3qYsWKqdQebdAei3uknR7qS0\naEQc+9rqwk9uikcc2s2m3vSseEd7DfUj4v5fxY9/1uL4UxFYVwp+5DXxF++PiPj0O+Pg48dK\nXt5SXRms5qRTqh0AACi00AtlfyoAzI3Gk9xf/3Ac/ZLq8vT0dF1aNHnmsuK0aN1qLzwsrr5x\nxlO/vLWaFv2fm+Phj4+IeOqj4/rbO8iMXvKK6sJsMqP/cG51oaPM6MaNG9OCzCg9odoZZN4d\ngL561kh9WjQi/mt1/O6q6vI999yTT4vmpcxom7+k+3rqujxnMLU9Fi2OmI6o1E7YJ42HVKnE\n2rXr2k+L1r228UotaEm1M8i8OwBzoPDKn3SNffsb+cL7amnROu87LV57ZddH19T9v6ou/Phn\nxVcuZZoVn5/eMR367HOjO409CAp3BCVUOwAAUMgt6QGYC40Ti1latFKZ0Vs0crfyLEmLZqu9\n8LCIiJMPn/FUlhbNxutvr45t3isz5US7SIvmZ6BSTrTTDqMpJ5pGd/YEAOhaSovmC6r/Wl0b\nY2Zv0SQ7Lf2k53dwKjpbM93ls85N/9zudgqlQrrZefpFiyMiKotq94XPaum8jRs3phUOOaSa\nFu20zsxv0ClXAIA2FaZF33HyinQP9zYd+9q485aCx993WnXs+RTinr9RHRuPP2tanxQWn7Ej\nJ9osLXrRqbXlf7u8eJ26MviyVxXvCAAAgE4t9CurXFsGMJeuuziOeeWMRxovDe+iX9HJh8fV\nNxa/MHUYbZwz7dPv/uwzpVI0Tdvpt1b3j1P+cn2eaEa1M8i8OwD9VldQbdoQa79R6zBap/17\nhtaVXqlxVJYWfee/1J7K0qKH/llHB16/8fKDyRR+qmQN7PfYY2lWWJas39L3ro0jTujmhSxA\nqp1B5t0BmBcn/V514Zr/iA+dGS+/pPVLsrTow1bWHjz7mLjwunjfaXHWVdVH2plCbNTR+lla\ntLum9UmWFj3rqlpa9A9Pry4UVr+Xv7q6cPrfdL1bFiLVDgAAFFrohbI/FQDmzHUXVxcaM6P5\ntGjSaX605TnvtEK6R2fJBqemphYtarf9duGmpqenm6VFy4+wfBflL3dLJkqodgaZdwdgDmQF\n1aYN1Ud237tgtY7SoknabP6F5x4/Iy2a3PTPs0qLpoWVK0fLrx362URExG8vL15h48aN37tm\naVp+6l9UX9Jsg9u2bVuyZEmzfX3v2uqCzCjtUO0MMu8OwHw56feqadGkzcxoXVo0ufC6iIhK\nJc49Lt7xLx1PEnYxqbhu3brZpEWTi06t5VxTXfqx18cp7ymryS9/tbQoHVPtAABAIbekB2CO\npJxoXVo0iu5uWZcfbRwbZS8svwFTy7RoNrbU7HgK06LR8E21KXtVs7s7tfMsAMDClM43ZzVS\nyokWpkUjd8vL229ssdm60it/r8w99yhYv+u0aLbZlStHI6JSiSMfUXtq+/bt37p6xiHVpUXz\nZefSpUtTTjSN0bx03LZtWzYWSjnRPVaMZafzAQBI2ommTU5OXvMfETtyovm0aEl9lU+Ljo2N\nnXrh2EVfqqZFI+Lc4yIi3nR8tca7/9fxwN1tHnBtbNPBBx+8bVPTZ9uc+cynRbPxY6+vvw19\nnrQoAABArwiMAtAX4+Pj2XJ2I87GtGijwvxom5nRuhVe9/SCTTVTqUTqLdpmh9EuplNnGess\nf6G0KABAXjrdXnfSvVlaNMnSom1mRvMvjIh3PT+ysdENVxc/3tLo6OjY2HhEPOXhEVHNjG7f\nvv271yyOiCwzWpgWraucs7RoidRbtKTDaOzIjLZsxQoAsKCktGizzGiqSycnJ5cvXzY5ORkR\nJx9RkBatK1//s6F7fUSMjo5mFxQl7/iX6viGVdVKdY/92j/s+kfKQ58pLVqYGS2fvC3ZexpP\neU+EIhMAAKD/FnorfjcjAOiHLC06MjKSpUVn01op2r5BUtZDNEuLvveLPds47IxUO4PMuwPQ\nV2NjY4Xnm8u7zt9+Yzzq8BZbvv7KOOq0gl286/nxxn8s2H6WFj3y5OINTkxMrFixomSP4+Pj\nIyMjRz4ibvhJ9ZGUGX1Kkw1G0bdZ/o1DP6h2Bpl3B6Dnpqen83cf+rmLsFkAACAASURBVO/P\nx+88IyIXA01Bz4h44Y6C8+rv1V5eV1tmadEnHB8R8ePvxmOeWFu5Uolf/igi4jceU33kDauq\nC+9e3f230M406bZNsWT3pi/P32fJ5wzzS7UDAACFdBgFoPdGRkayMeVE20mL/seny55tsz1n\ntkLKibaTFm1/45ny+29WKrVWTwAAzJfsdPun31l7sLzvUaXSVlo0jY1doFJatHH7KSd65MnF\nZeTExEQ2NtqwYUNEjIyMVCq1tGhELF5clhaNhuK2i4ZPAAB0pC4tmsb3nlK703qq0G65Zeyc\nK8ZiZlo0GpprppxolhbNxiRtKkuLxo6c6LtXx0df3/230M40abO0aP6Fik8AAICBJTAKQF+k\ntGjSflq0MDPaznXt17yp4ME206ItN16n8P5QSaVSPdojT6lmRle/NyLizKM6OBIAAHoopUWz\nzGjhKfDTj4xo+6x26i161Gm1E//ZU9u2bctCAHXlYpYWbSwjU2/Rwg6jKS26YcOG2Z9xLz/3\nf//993e/aQAAGqTeol/6RETUMqMRMT1dXa5Lixba/eA1aSH1Fs13GI2ZadEkS4s2ZkbbryR7\n0pCx0+vzWyq/gB8AAID2CYwCMA8aJyh/79m1sXHN8hPkKS2az4yOj4/P/iCbaUwGJPnDu+Fj\n8ZSTZ6RFZUYBAObFs8+tjUlhWvT0Izs4q50yo9GQFo1cZrSxXGxWRkaTtGhE7L333mmcmmr3\n2BplJ9fL06Kzz4zqIAUAkPc7z4jXfSwiqmOm7tbzjVJZ9YvJiIg1a2ZkRlt60XtqY90G+12t\n1WU6U/GZ+uj/7Gc/azyeTrcsMwoAANATlekeXt+3E6pUFvq/AMDca6djaGZsbGzlytHp6Rav\nuuZNcdI7qstZWjTf5bSZTRti971bH0abKpX6w1v93lj1ujjzqLjk+p7tpdxZfxoXfWmO9sVO\nQbUzyLw7AHNm3bp1Bx98cPblJ98ez3tL7dnTj4zLb6h/yWEPje//rP7BiLjvf+NBvxURsWnT\npt13n3E/zm3bti1ZsqRXx5ypu7NnR7LT6uWhhPvvv3/PPffseOu5ArijIp+FQ7UzyLw7AHNg\nYmKi7tKgluVZVlb9fE1ExEOW9eZIGuct87Zv37548eLZbL/w+0pp0f322y99+dCHPjS6rRtb\npmyhkWoHAAAK6TAKwFxrv3VTmme85ZaxZtOI2eMpLZrWTznRNtOi2dgTb3/ejKOKiFWvi4g5\nTYtmIwAAybp167IxIj759tqYXPy5+pcc9tDamHff/1bHTZs2RVTHTMu06K3f6uS4d5hNR6iS\ntqZ5XadF07hx48YHHtgY0qIAADkpLpnGTMvyLD93mk+LlleDjd03v/+p+OsX1G+20Pbt27Ox\na4XfVwrLppxoGqPbu9VLiwIAAPSKwCgA/XLGkTO+fM7v1JbbnBDM5hnT+lNT08flLsivO3Ge\nvzNRs7ToQfvM+DL1Fu26w2jdPOz5JxQc1RxLvUV1GAUAyGzfGqm3aNZhNPUWzTqMPnB3bYwd\nNV7qLdrYYTT1Fn3Qb0XqLVrXYbRcSovOJjPanezk+j++qfuN5NXd4356OpYuXRrSogAAM6W4\nZF2H0WjIPjZmPZulRZvVhI13bP/+p+Jf/znecM2MzGgzqbdopx1G//X/1T9SmOnMZ0YzU1P9\nKhzna1YWAABgJ7LQW/G7GQFAn2Rp0ctuiMilRf/pv+vXXLNmzfLly8u3VqnE1NT08SPVCb/P\nTtQef/2xccHnIyK2bds2OTlZcq15lhb94fr6p370rTjwKeWHUK/wLkvnnxBvvrbFDZ5gjql2\nBpl3B6Dftm+tLizepWy1B+6OPfaLaKjxOqrrTn1SXPWdFuvc+q14bIdlZ6PsdpyFh7d169Zd\ndin4brO06PPfUbzZtV+PQ45ua+9pQZMn2qHaGWTeHYC5sf6nsc8BTZ9tv7gqL00b79hecsum\nlr/+0zold4FPadFt2+KYV7bYVKPs06fS03Tn3Xff/eAH77djFz3cMDsx1Q4AABTSYRSAvnjV\npWOxIy0aO3KiWVo0mwxcs2ZNNjaTVl60qPIv49ORS4tGxOuPjYg45xmxbdu2iFi2bFnRBqpS\nTrQxLfrSP4iI+FFRq6cv/k3TrRXeZenN10aYkQQAGBgpJ1qeFo2opkVjZo3XUef4U59UG0uU\npEXf9fy2dpS1jyo8vK1bt2ZjnZQTLUmLZmO5Nu9xDwBARKz/aUTED79Z/Ozs06JbN0VEfOyc\ngi0U3vm9nRI3v05j99Pk//xlbNsWEXHdxeUHXrj9SvQhLRoRv/713WFuFgAAoBWBUQB6L80k\npsxo7JherEuLpjH1Fl2+fHn68oWH12/qotNqk5uVSiWfFo2o9ha94POxZMmSiOpYMtnYLC16\nwTkFHUZTWrRlZrQ7b39e1y9t1yH79n0XAACDr2VatE5W4xWeYi9UqVR7ixZ2GG1WnW7fvj1b\nTmnRxszoL9YWH97o6Gjh4aXeorvsskvhTpulRSNi62+NRbTVYTTaKIN/+t9xx01tbQoAYLi9\n6tnxvz+OaJIZbfNSnGYpzywtmo0RMTExka3cWMq2U+Lm1xkdHc1nRtffWVst9RYt6TD6lEc0\nfaq3adGI2G+//dIoLQoAANCSwCgAvVfemaluXnLFiuX5dfKZ0YtOq44lM30pMxoz06LtTzle\n+u8REYccVPDU019dG3srpUVLMqPvf/Fsd5HSojKjAACz0awKzZ81z+rPq75TUIU2q05TWjTL\njL7xH2tjJqVFCzOjzRIAkUuLtl8Sp28nZUYbdXo2//orY/u2iJAZBQAWulOfGBHxjrMiIg76\n/eJ12rki/cC9amPeLrtHRJxyQW2cmJg457kryqvBdiKVdWnRNKa0aGNmtFBKi5ZkRnvr8ldX\nM6MAAAC0JDAKQF+c/MdlnZnSlxs2bGgMkv79jbVHzrqyNrap/UZQmZQWfd9pBU/1Iy0aEW/5\nZG1slNKis8yMrr0n1q2PtffMaiMAAAtQs9tu1q2QjbfcMhbVdvgRO07Mv+Cw6spZdTo5OZnf\nyOLFi5csWbx48eLskZQWnZ6qrfOQQ2pjZnp6umUetNOSuKS1VafZ0+uvjIi47b8iIh55aNxw\ndbsvBAAYMmNjY2d/ZCwirvpu07RoXknFdeuGOHCvuHVDwVP5zGhEnPPcFRHxzOURHU6QNquB\n84XiPg+LNE5PxdcubbHBb/2kNvbb5a+ujQAAALRUmV7Yt2eoVBb6vwBAPzzhIdWF//zFjMfH\nxsay89AbNlTnOPfZZ+95/038vtPitaWx1PNPjDd/ovV23v/ieM0VPTie2W8nm2Ke939b5p1q\nZ5B5dwAGTXamvLzVU76szS9XKjE9XUuL/sP3qwtZWnTZsmXZmkn+cyBLi1aaXN6bfWosWlRJ\ni1NTU4sW1dZOB9Co2ePt6PS1118ZR50WEbW06JEnd7lrhoBqZ5B5dwD6LV8oluvhVN6qFbF6\norOXtFkDJxefEaM7yt0/emlnO+qfy18dp//NfB8Eg0e1AwAAhRZ6oexPBYA+ecJDqmnR7ARz\npRKp/VI+M7r33nv39TDWrFmzfPnyWW7k/BOrC+WZ0awnaGPWczZn6LuW32n709MMH9XOIPPu\nAAygNgunuqRmnRccVkuLJpOTkyktmi+PGz8EpqeapkWrK0xPV3YECqamqgnTdCTNcgbzeCnR\nDVdLiy50qp1B5t0B6J8u5uL6N3+4du3aQw45pHydNg/44jOqC6OHDVBaFJpR7QAAQKGFXij7\nUwGgrxpvpZRu1jk3v3rXrFmTFnqSGW2WFs1PpxZ2Bp33Zp8dNQlg+Kh2Bpl3B2Cns21TLNm9\nPqnZaNOG2L3owqieVIb5crpZh9G6kruxAt98X+z2oO6PIe+vXxh/9fczHvn+P8Vhz+nNxtnZ\nqXYGmXcHoE/an4trFtOc5fRp/hL6tWvXpoWWmdE2XXxGvPKynmwJ+k61AwAAhRZ6oexPBYB+\nqztpPcfpyZIOo7PPrWY9U6PV/G/hvvodnD32kPhCdUJYh9EFTbUzyLw7AANi66bYZffWq23b\nVF1ImdHCtOgnzovjz6ouN8uMlnQYbamxnG7cTt06B+4dP9owY4XN91UXZp8Z/esXVheyzOj3\n/6m6IDNKqHYGm3cHoH/amYtrlivtevo0lYWNl9C302G0XHlz/TaNj4+PjIzMciPtu+eee/bd\nd9852x2DSbUDAACFFnqh7E8FgLk3L/dnbzyGpOsjybZwyy3dZDH7HZw9dsckcJYZZcFS7Qwy\n7w7AINi6IwbaZmZ0SfPVPnFedeH4s4rTosk9P439Hl5dbvY5MP7lGPmT4qfy5XRjVXntWyMi\nTnxbLS2aNGZGZ5kWve+++x70oAdFaYfR/7k5Hv74We2FnZ1qZ5B5dwDm2NS2WLRkxiMpV/rA\nr2OPB894vIvp03xZWHIJfRfKm+u3eZn6+Ph4WpibzOg999wTEbtX9t19nznYG4NLtQMAAIVm\ne0UgAGSe8ojqQuOd6PPmZYpmYmKi8RhmcyTZFrrr3Dn7AyiXcqLSogAAmSteW/x4yom2kxaN\nKEuLRsSJb6uO5WnRiLj7fyJK06LZ2Cj/qsYtnPDWGY+nnGg+Lbp9S0QnvUXPfnrBg/fdd182\n1qVFI2pp0WwEAFjgprbVxkxKi0ZEGjMlc4bN5l3TSyYn165du7aHadHYkRNtlhbdZ+to1iq1\nRMqJzlmH0X333Xf3yr4RsWn93OwQAABgZyIwCkBvpLToUx5RnbXMz122H4vc8PNeH1ZE7EiL\nFmZGy/Uj+fr6Y2f18vZJiwIAC9P//rC6kK/lUlq0PDNaLr+1kioxZUabvTAi9j2gOpZUg6m3\naLMOo3lZw6f8XlJmNNOYFk1jO1JatDEzmnqLprGZ9P39WGAUACCqvUXrOoxGxNJ9I6K+w2gz\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meQeXcA5sv2LdWFxbsWPPuLyXhIw0zk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PjUnZrRNa9j5snhm6UXGzZsGDt2bE1Nzbff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izFYDACrJ0UzUHSFFeGhvUZ8Oo+ZPgCCqsh+9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odmpELQq0FK2jgjS89L8M76xzTKSy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\nRcWiDNfXfqu/bNEkdkalj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"text/plain": [ "plot without title" ] }, "metadata": { "image/png": { "height": 900, "width": 1800 } }, "output_type": "display_data" } ], "source": [ "options(repr.plot.width = 30, repr.plot.height = 15)\n", "\n", "\n", "p=splitFeaturePlot(obj.integrated, \"isg_score_small1\", split.by=\"cond_tp\", title=\"FeaturePlot\", ncol=4)\n", "save_plot(p, \"split_featureplot_isg\", 30, 15)\n" ] }, { "cell_type": "markdown", "id": "748b091a", "metadata": {}, "source": [ "\n" ] }, { "cell_type": "code", "execution_count": 26, "id": "543cd533", "metadata": { "fig.height": 8, "fig.width": 12 }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[1] \"SYMPTOMATIC Ctrl 0\"\n", "[1] \"Removing group SYMPTOMATIC\"\n", "[1] \"ASYMPTOMATIC Ctrl 0\"\n", "[1] \"Removing group ASYMPTOMATIC\"\n", "[1] \"CONTROL TP 1 0\"\n", "[1] \"CONTROL TP 2 0\"\n", "[1] \"CONTROL TP 3 0\"\n", "[1] \"Removing group CONTROL\"\n", "[1] \"Existing Cells 14672\"\n", "[1] \"New Cells 0\"\n", "[1] \"TP 1\" \"TP 2\" \"TP 3\" \"Ctrl\"\n", "[1] \"SYMPTOMATIC 3\"\n", "[1] \"ASYMPTOMATIC 3\"\n", "[1] \"CONTROL 1\"\n", "[1] \"TP 1 1.10543539557871\"\n", "[1] \"TP 2 1.04267403662383\"\n", "[1] \"TP 3 0.952347915061169\"\n", "[1] \"Ctrl NA\"\n", "[1] \"TP 1 1.03600907843311\"\n", "[1] \"TP 2 1.10322999910783\"\n", "[1] \"TP 3 1.00194124153661\"\n", "[1] \"Ctrl NA\"\n", "[1] \"TP 1 NA\"\n", "[1] \"TP 2 NA\"\n", "[1] \"TP 3 NA\"\n", "[1] \"Ctrl 1.01263554921205\"\n", "[1] \"Creating Plot\"\n", "[1] 4 16\n", "[1] \"comparative_violin_isg 12 8\"\n", "[1] \"Saving to file comparative_violin_isg.png\"\n", "[1] \"Saving to file comparative_violin_isg.pdf\"\n", "[1] \"Saving to file comparative_violin_isg.svg\"\n", "[1] \"Saving to file comparative_violin_isg.data\"\n" ] }, { "data": { "image/png": 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y70EOiYIpxRxXhDIYFGaHShh0DHRKCBGl3oIdAxEWigdvix6GW/RQQ6JgIN1Ah0\nD4GOqR3o3wg0do1A9xDomP46DFuyXAKNNSLQPQQ6pr9GLFkugcYaEegeAh0TgQZqBLqHQMfE\nFAdQI9A9BDqmvw4/G340PyHQ2JvDbwS6i0DHFCHQQZcBWCDQfQQ6pkCBVpXkYnxmbwKNwAh0\nH4GOKXign0wLvaNAM/UpA4HuI9AxBQr0VZ2eWb6f1FeWqsu8ZWwZgZaBQPcR6JiegR443KjT\nQB/Vo7za5rRXBBqBEei+HQX6n//8Z4RHHZUH+qeO20C/o5z/S6D7CLQMBLpvP4H+569fv6QV\nOlCgT+8pjlP2na9Fz1nGlhFoGQh0324CnfdZXKHNAl1t33tfat6gXW7n2nvy2ofj/vyST8Pv\nbT+BPvxNF0Qg0H17CvT5vM5A51rnJGz+Y3ROwsfHUalj+njeYnxeQgKNwAh0H4GOKVSg5yDQ\nCIxA9+0m0M9Cn8/C+rww0L3L41daItAIjED3xdk+Emcvjv/5nwiPOsp8N7tlgf5MsuxbJR82\n3xuBRmAEum9HgX6uQUd41FHWa9DtjYSZ4UbCz+fdig2FNoXeVaD/JgwCEOi+/QT617//8z9/\nhX/YUTOnOPo3jF95VN/P/z5vKrH43gj0nkXpAoHu202gf/37Geh/Cyt0oEA/V6CvVu8iHFzw\nJhHoPgItBIGOaVmgjffiSNT9om75LLTF90ag94xAC0GgYwoU6I/8TSr5CrTxTtBDC94kAt1H\noGU47CbQW5yDNn0nYZaq5PpckbbpM4HeNQItw+GP3QRa6l4cAY5mF20Zq3D4m0D3EGgZ9hTo\nbNmJWH3glFcCEGiNaIFmIFp2FWh5v55DBLo+xtLQhMj0MjaNQGvECvRvDERLtEDHeFQCTaA1\nCLQGgZYhTqAjzTVJD/RPL4GOuYxVINB90bpAoNsIdFQEWoAy0OJeG1ERaCEIdFR/HYYtWS6B\ntlAEmlXoFgItBIHeJAJtgUD3RenC4TcC3UWgN4lAWyDQfQRaCAK9SQTa3IFA9xFoIfKBiDES\nBNorAm3u2WcC3UWghXgOxB8EenMItDkCrRGlCwS6j0BvEoE2R6A1CLQMBwK9SQTa3CvQFLqJ\nQMtw+CNaoCMMBIEOvYw1INAaewr0QfARmuIEOtZvSgIdehkrcCDQGvsK9LK37fpEoLeJQBvL\n+0yguyIGOvzDEujuoxJozwi0sTrQu3l1TIvYhfBhINC9RyXQnhFoY1WgWYWu7SnQBwLde1QC\n7RmBNkagNaJ04ZmFKIHOj8MudeyLgYgxEpG21gZ/zEgItKmizwS6g0ALQaC3iUCbItA6ZaAD\nPyEEuuc5DgR6iwi0qUagKXQlyoobge6JGugIW2uFDoN7BNrQgUDrEGgZ4gQ60kAQ6PDLEK/s\nM4HuiBno0GEg0NBy5Y0AACAASURBVL1HjRZoqePgHIE21Ao0hX6LEeg8CwS67RXoCCNBoL0y\nj2vy1LxcfUKg9yvKihuB7iPQG2Uc16T60PjXchkr9uozgW4j0DLk40Cgt4hAm+kGei+vjwmR\nAx34cQl092EJtG92cU1a/8xbxjp1As0qdOkd6KBPRxXowGUg0L2HJdC+zQt0NQV9yO0g0O8+\nE+i2GFOfRZ8JdEsV6LCFJtDeWcW1NcOxq42EvUBT6AKBFqHoc/hAx9oYQKAHJPpPth/oA4HW\nI9Ai7CzQog8r6JhNXJOBz3YQ6L/7gd7LK2RU7ECH/YOeQHceNuLuNCLHwQOLuCbtSzsPNKvQ\nWaRtU41Ahy3DKgIdciiiBlrkQLhn8UaV9sXGp5sPdN1nAt0SI9Blnwl0w2GHgRY5EB6Y7wf9\n3nUjydrvKtxtoHfyChlDoEUo+0ygt4hjcUw7aAPNKnQz0OGeDALdQ6C3i0BPa/S5FeidvERG\nRNj99tAKdNA0iO1CO9CBh4K3dHpFoCcdBgLNKnSUQP9GoDtefX4FOvRQhN/f8SB1IHwg0JOa\nfW4HeievkWG7CrTYLuwu0D+FDoQPBHrKYTDQu1+FPuwr0EUXBIZhn4EWOBA+EOgprT4T6KYI\n7484dAIdMA1iu9AJdLixIND+EegJh5FA773QMQL9G4HuePeZQG8RgZ7Q7nM30Pt4kQwh0BIQ\n6C0j0OMOo4He+Sp0M9CBngkC3dMPdJhv8RDroChSB8ILAj2u0+duoPdd6AjvMO4HOtw2MaFd\n6AU60FjEesfQQepAeEGgR3X7TKCbwge66nMV6HBpENqFqs+7CfRPmQPhB4Ee053g6Ad614Um\n0ALsNtDiRsILAj2m12dNoHfxMtGKcBA1At1DoDeNQI/o97kf6B2vQkc4Ro8u0MF2WlhNoMMM\nBoEOgUCPMAv0Ll4nOjICHawNMgNd9zlwoNtHrQp5OEMCHWEZEmn6rAn0fgsd/iBqdZ8J9Eu8\nQLcHIuAx9Ah0hGUI1N9CqA/0bic5WoEOGoUYga727pI13PsL9E8CHWEZ8mj7rA30Xgu9s0DL\n7II20CEGI36ghY2EHwR6iLbPA4HewyulT0igA7VBZqAPBHrbCPQAfZ/1gd7nKnT4o1w2+kyg\nS40+hw10tPfcE+goy5BGP8ExFOhdFppAC0CgN45A6w30eSjQeyx0N9ABo0Cg3wj0xhForaEV\naAJdC3+ceALdMxBo/4NBoMMg0DqDfR4M9A4LLSbQQdrQ2LtL0FA3+xw00PHec0+goyxDlsE+\njwR6D6+WpkPwQDf7HDHQkrqwv0AX4yBvILwh0BrDfR4O9O4KHf5EHgS6h0BvHYHuG57gGAv0\n3iY5CLQAOw+0oJHwhUD3jPV5LNA7K3T4E3kQ6J7BQHsejHhv6WwGWtJI+EKge8b6PB7oHbxe\naoICHSANMrvQ6jOB3iIC3TXa59FA72oV+hA80K0+E+gcgd48At21JNA7eMG8hT+RB4HuiRXo\ngz7QwaaaxA2EPwS6Y7zP44He0yq0LtDB1toIdGks0D6/yYjvGJI5EP4Q6LbRLYSTgd5RocOf\naWks0N6nv9tdkDLMI4H2OhgEOhgC3TbR58lA7+AlUxIVaO9pKLPw7oKUMBwiBTrm7jQEOs4y\nhJjq81Sgd7MKHf5EHgcC3dXu8y4CfSDQcZYhxOJA76XQEQLdKjKBzvYZ6J8EOsoyZJjss0Gg\nd/CiyQYCHWzeM3SgX1moAi2kDJECHXNrbSfQMsbBJwLd5CDQO1mFJtACjAba32DICbSQcfCJ\nQDdM95lAlyIcJ55A9xBoGePgE4FucBLoXRRaWqD9luHd5zrQIsrQ6TOB3iICXTPoM4EuhT+R\nR6fPBDpaoNlaGxKBrjkK9B4KTaB9Pp6hHQa6O9ckYRj8ItAVkz4T6MJhV4E+6AItYJAJ9A4K\nTaDfpt7kbR7o7Rc6wnHiYwb6Zz/QEsowEWhPgxHzTxkCHW0Z8Rn12TDQW3/ZDAc60JYpAp0R\naCHD4BeBfnMY6M2vQkc4kcdEFzyG4aAPdPQx7vZ5B4E+EOhYy4jOrM8EOncIH+huFkIG+qcu\n0PHTsMdA9wYi/u9Jzwj0i9NAb7zQEY4TT6D731eUQEccCLG70/hEoEuGfSbQuQjHiY/XhUaf\nW4GOngYCLWEUfCPQBbNdOMwDve1CRzgMMYHuf2NTgfYyFjEDrd3f0d/DiUCgC6Z9Ng/0ll84\nBFpCGnYYaN1AxB4Gzwh0zngF2jjQW16FjnGUy8ku+ApDs8/tQEdOQ6/PQQJ9mA50iK0BBDr0\nMuIy7rN5oDdc6AiB7mWBQEcKdLSBGNzf0dfDyUCgM5s+2wR6s6+cCEe5jNaFVp87gY7bhh0G\nWjsQ2/05KxDozFOgt7sKTaAJtJxAb3wVmkBb9dkq0Bt95cQ4Rk+sLhzGAx1zhE0C7f77ixjo\nA4GOuIyIzLcQ2gV6q6vQUgPtZZ+FdpE7gY7aBoNAexiLmIEeGIitrgiVCLTVCrRVoDda6BjH\n6DHogpcwEOjOg8b6TZkJ3t/RKwJt1WfLQG/ypRMh0P0shAn0YSrQ8Qa43+cggY40EMVjE+iI\ny4jGaoLDMtCbXIWO8Q7jaIHuBLkb6Iht2F2gh/d33OZ60AuBtiquZaC3WOjpQIeY+QzShe4K\ntCbQ0QaYQEv4Nenf3gNt2WcCHeUdxkZdcB+Gbp/7gY7XBgItYBACINBeA729Qh8iBFqTBQJN\noOMPQgA7D7Rtnwl0jPdHRAp0r8+aQEeLQ5RAa3bi0AXa//40BDr0MiLxHujNFTrG+yNMA+34\ncQl070Hj/KYsHnpsILb2Q9aw70Bb93lGoDf24omw+61uvU3XBcdh6PdZF+hYcSDQ8ccghF0H\n2nIXu1mB3tgqdH8KOkCgNVkg0CaBDjESBNqrfQfaurZzAr2pV0+MFbc4ge7tYzcU6DjjGyPQ\npn/K+NjRkkDHXUYUQQK9rVXoKF0wDbTTQmv6rA10pDqIHggPgR4diE39iLXtOdAz+kygw3dB\nu96290Brppp2HOgNF5pAew/0lgqtyYL/QOuy4D3QuhmOgUDHGN8YA0GgIyDQBNqCty788M/y\n/1TXYm2go9Qhym/KOH/KlI9NoOMuI4I5fSbQRl2wD8OP/xryH/+hXYPW+4/BxfzX6gIt73eW\n9ikn0F4RaAJtIUagLfrsLtDaPg8E2lMefvzviH9onnSNf4wt438JtHwE2n+gt1NoXZ8JNIH2\nHujJA3M7fjw5CDSBNic+0COFJtALA63vsz7Qrgs9ddzXzfyE9ew30DPeRkigTQNtXejBQNv1\n2VWgtftwDAfaywAT6MZjE+jIywhvVp8J9E4CrS/xQKD99CFKoANsmbScWClMBnozP2JdBJpA\nG9O9O0JaoAcLTaCNAj34Cy7SxoCS4HOPeUagCbQxbZ9lBXq4DAR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SFoF2MwZmAzE8GgR6muBAi0y0uEAf/Gz1iLfiVj788i6YBDqAJYNQ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\nrQSWOKQ5iiMAAAAASUVORK5CYII=", "text/plain": [ "plot without title" ] }, "metadata": { "image/png": { "height": 480, "width": 720 } }, "output_type": "display_data" } ], "source": [ "options(repr.plot.width = 12, repr.plot.height = 8)\n", "\n", "p=comparativeVioBoxPlot(obj.integrated, \"isg_score_small1\", group.by=\"condition\", split.by=\"tp\", yStepIncrease = 1, verbose=FALSE)\n", "save_plot(p, \"comparative_violin_isg\", 12, 8)\n" ] }, { "cell_type": "code", "execution_count": 27, "id": "ea74ac61", "metadata": { "fig.height": 8, "fig.width": 12 }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[1] \"SYMPTOMATIC Ctrl 0\"\n", "[1] \"Removing group SYMPTOMATIC\"\n", "[1] \"ASYMPTOMATIC Ctrl 0\"\n", "[1] \"Removing group ASYMPTOMATIC\"\n", "[1] \"CONTROL TP 1 0\"\n", "[1] \"CONTROL TP 2 0\"\n", "[1] \"CONTROL TP 3 0\"\n", "[1] \"Removing group CONTROL\"\n", "[1] \"Existing Cells 14672\"\n", "[1] \"New Cells 0\"\n", "[1] \"TP 1\" \"TP 2\" \"TP 3\" \"Ctrl\"\n", "[1] \"SYMPTOMATIC 3\"\n", "[1] \"ASYMPTOMATIC 3\"\n", "[1] \"CONTROL 1\"\n", "[1] \"TP 1 1.10543539557871\"\n", "[1] \"TP 2 1.04267403662383\"\n", "[1] \"TP 3 0.952347915061169\"\n", "[1] \"Ctrl NA\"\n", "[1] \"TP 1 1.03600907843311\"\n", "[1] \"TP 2 1.10322999910783\"\n", "[1] \"TP 3 1.00194124153661\"\n", "[1] \"Ctrl NA\"\n", "[1] \"TP 1 NA\"\n", "[1] \"TP 2 NA\"\n", "[1] \"TP 3 NA\"\n", "[1] \"Ctrl 1.01263554921205\"\n", "[1] \"Creating Plot\"\n", "[1] 4 16\n", "[1] \"comparative_violin_isg_custom 12 8\"\n", "[1] \"Saving to file comparative_violin_isg_custom.png\"\n", "[1] \"Saving to file comparative_violin_isg_custom.pdf\"\n", "[1] \"Saving to file comparative_violin_isg_custom.svg\"\n", "[1] \"Saving to file comparative_violin_isg_custom.data\"\n" ] }, { "data": { "image/png": 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55zIBXnxjGhb+XhoJ9dvpeXrv37fxU3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\nAElFTkSuQmCC", "text/plain": [ "plot without title" ] }, "metadata": { "image/png": { "height": 480, "width": 720 } }, "output_type": "display_data" } ], "source": [ "options(repr.plot.width = 12, repr.plot.height = 8)\n", "\n", "dsrCols = list(\"Ctrl\"=\"steelblue4\", \"TP 1\"=\"darkred\", \"TP 2\"=\"chocolate2\", \"TP 3\"=\"darkseagreen3\")\n", "\n", "p=comparativeVioBoxPlot(obj.integrated, \"isg_score_small1\", group.by=\"condition\", split.by=\"tp\", yStepIncrease = 1, verbose=FALSE, dsrCols = dsrCols, boxplot_grey = \"ivory4\")\n", "save_plot(p, \"comparative_violin_isg_custom\", 12, 8)\n" ] }, { "cell_type": "code", "execution_count": 28, "id": "fc3ff86f", "metadata": { "lines_to_next_cell": 2 }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Centering and scaling data matrix\n", "\n" ] }, { "data": { "text/plain": [ "An object of class Seurat \n", "39601 features across 14672 samples within 2 assays \n", "Active assay: RNA (36601 features, 0 variable features)\n", " 1 other assay present: integrated_gex\n", " 2 dimensional reductions calculated: igpca, umap" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "obj.integrated = Seurat::ScaleData(obj.integrated)\n", "obj.integrated\n" ] }, { "cell_type": "code", "execution_count": 29, "id": "155cc29f", "metadata": { "lines_to_next_cell": 0 }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Scaling data GLOBAL[1] \"Fetching global scaled average expression\"\n", "[1] \"combined_lists\"\n", "[1] 0\n", " [1] \"MT2A\" \"ISG15\" \"LY6E\" \"IFIT1\" \"IFIT2\" \"IFIT3\" \"IFITM1\" \n", " [8] \"IFITM3\" \"IFI44L\" \"IFI6\" \"MX1\" \"IFI27\" \"IFI44L\" \"RSAD2\" \n", "[15] \"SIGLEC1\" \"IFIT1\" \"ISG15\" \n", "[1] \"Clusters Missing\"\n", "numeric(0)\n", "[1] \"Genes Missing\"\n", "character(0)\n", "[1] \"Adding missing clusters\"\n", " [1] \"1\" \"2\" \"3\" \"4\" \"5\" \"6\" \"7\" \"8\" \"9\" \"10\" \"11\" \"12\" \"13\"\n", "[1] \"heatmap_isg_genes 12 8\"\n", "[1] \"Saving to file heatmap_isg_genes.png\"\n", "[1] \"Saving to file heatmap_isg_genes.pdf\"\n", "[1] \"Saving to file heatmap_isg_genes.svg\"\n" ] }, { "data": { "image/png": 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RrQgAY0oDMN0IAGNKABnWmABjSgAQ3o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\nQEtSpgFakjIN0JKUaf8/HP5DY0bJTzkAAAAASUVORK5CYII=", "text/plain": [ "plot without title" ] }, "metadata": { "image/png": { "height": 480, "width": 720 } }, "output_type": "display_data" } ], "source": [ "options(repr.plot.width = 12, repr.plot.height = 8)\n", "\n", "plotGOIs = list(\"1\"=list(\"clusters\"=c(0), \"genes\"=isgGenes))\n", "\n", "p=enhancedHeatMap(obj.integrated, plotGOIs, group.by=\"idents\", include_all_clusters = TRUE, title=\"Heatmap of ISG Genes\", scale.by=\"GLOBAL\")\n", "save_plot(p, \"heatmap_isg_genes\", 12, 8, save.data = FALSE, draw.fun=ComplexHeatmap::draw)" ] }, { "cell_type": "markdown", "id": "a72effb1", "metadata": {}, "source": [ "\n" ] }, { "cell_type": "code", "execution_count": 30, "id": "d78b67fd", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Scaling data ALL[1] \"Fetching average expression\"\n", "[1] \"combined_lists\"\n", "[1] 0\n", " [1] \"MT2A\" \"ISG15\" \"LY6E\" \"IFIT1\" \"IFIT2\" \"IFIT3\" \"IFITM1\" \n", " [8] \"IFITM3\" \"IFI44L\" \"IFI6\" \"MX1\" \"IFI27\" \"IFI44L\" \"RSAD2\" \n", "[15] \"SIGLEC1\" \"IFIT1\" \"ISG15\" \n", "[1] \"Clusters Missing\"\n", "numeric(0)\n", "[1] \"Genes Missing\"\n", "character(0)\n", "[1] \"Adding missing clusters\"\n", " [1] \"1\" \"2\" \"3\" \"4\" \"5\" \"6\" \"7\" \"8\" \"9\" \"10\" \"11\" \"12\" \"13\"\n", "[1] \"heatmap_isg_genes_ALL 12 8\"\n", "[1] \"Saving to file heatmap_isg_genes_ALL.png\"\n", "[1] \"Saving to file heatmap_isg_genes_ALL.pdf\"\n", "[1] \"Saving to file heatmap_isg_genes_ALL.svg\"\n" ] }, { "data": { "image/png": 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xrQgAZ0oQEa0IAGNKALDdCABjSgAV1o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", "text/plain": [ "plot without title" ] }, "metadata": { "image/png": { "height": 480, "width": 720 } }, "output_type": "display_data" } ], "source": [ "\n", "plotGOIs = list(\"1\"=list(\"clusters\"=c(0), \"genes\"=isgGenes))\n", "\n", "p=enhancedHeatMap(obj.integrated, plotGOIs, group.by=\"idents\", include_all_clusters = TRUE, title=\"Heatmap of ISG Genes\", scale.by=\"ALL\")\n", "save_plot(p, \"heatmap_isg_genes_ALL\", 12, 8, save.data = FALSE, draw.fun=ComplexHeatmap::draw)\n", "\n" ] }, { "cell_type": "code", "execution_count": 31, "id": "0f5b460e", "metadata": { "fig.height": 10, "fig.width": 30, "lines_to_next_cell": 2 }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Scaling data GLOBAL[1] 0\n", " [1] \"MT2A\" \"ISG15\" \"LY6E\" \"IFIT1\" \"IFIT2\" \"IFIT3\" \"IFITM1\" \n", " [8] \"IFITM3\" \"IFI44L\" \"IFI6\" \"MX1\" \"IFI27\" \"IFI44L\" \"RSAD2\" \n", "[15] \"SIGLEC1\" \"IFIT1\" \"ISG15\" \n", "[1] \"Fetching global scaled average expression\"\n", "[1] \"Clusters Missing\"\n", "numeric(0)\n", "[1] \"Genes Missing\"\n", "character(0)\n", "[1] \"Adding missing clusters\"\n", " [1] \"1\" \"2\" \"3\" \"4\" \"5\" \"6\" \"7\" \"8\" \"9\" \"10\" \"11\" \"12\" \"13\"\n", "[1] \"Removing clusters because they're not represented in subset\"\n", "character(0)\n", "[1] \"Fetching global scaled average expression\"\n", "[1] \"Clusters Missing\"\n", "numeric(0)\n", "[1] \"Genes Missing\"\n", "character(0)\n", "[1] \"Adding missing clusters\"\n", " [1] \"1\" \"2\" \"3\" \"4\" \"5\" \"6\" \"7\" \"8\" \"9\" \"10\" \"11\" \"12\" \"13\"\n", "[1] \"Removing clusters because they're not represented in subset\"\n", "character(0)\n", "[1] \"Fetching global scaled average expression\"\n", "[1] \"Clusters Missing\"\n", "numeric(0)\n", "[1] \"Genes Missing\"\n", "character(0)\n", "[1] \"Adding missing clusters\"\n", " [1] \"1\" \"2\" \"3\" \"4\" \"5\" \"6\" \"7\" \"8\" \"9\" \"10\" \"11\" \"12\" \"13\"\n", "[1] \"Removing clusters because they're not represented in subset\"\n", "character(0)\n", "[1] \"heatmap_split_isg_genes 30 10\"\n", "[1] \"Saving to file heatmap_split_isg_genes.png\"\n", "[1] \"Saving to file heatmap_split_isg_genes.pdf\"\n", "[1] \"Saving to file heatmap_split_isg_genes.svg\"\n" ] }, { "data": { "image/png": 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"text/plain": [ "plot without title" ] }, "metadata": { "image/png": { "height": 600, "width": 1800 } }, "output_type": "display_data" } ], "source": [ "options(repr.plot.width = 30, repr.plot.height = 10)\n", "\n", "\n", "p=makeComplexExprHeatmapSplit(obj.integrated, plotGOIs, group.by=\"idents\", split.by=\"condition\", include_all_clusters = TRUE, title=\"Heatmap of ISG Genes\", scale.by=\"GLOBAL\")\n", "save_plot(p, \"heatmap_split_isg_genes\", 30, 10, save.data = FALSE, draw.fun=ComplexHeatmap::draw)\n", "\n" ] }, { "cell_type": "code", "execution_count": 32, "id": "134f322a", "metadata": { "fig.height": 10, "fig.width": 30, "lines_to_next_cell": 2 }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Scaling data ALL[1] 0\n", " [1] \"MT2A\" \"ISG15\" \"LY6E\" \"IFIT1\" \"IFIT2\" \"IFIT3\" \"IFITM1\" \n", " [8] \"IFITM3\" \"IFI44L\" \"IFI6\" \"MX1\" \"IFI27\" \"IFI44L\" \"RSAD2\" \n", "[15] \"SIGLEC1\" \"IFIT1\" \"ISG15\" \n", "[1] \"Fetching average expression\"\n", "[1] \"Clusters Missing\"\n", "numeric(0)\n", "[1] \"Genes Missing\"\n", "character(0)\n", "[1] \"Adding missing clusters\"\n", " [1] \"1\" \"2\" \"3\" \"4\" \"5\" \"6\" \"7\" \"8\" \"9\" \"10\" \"11\" \"12\" \"13\"\n", "[1] \"Removing clusters because they're not represented in subset\"\n", "character(0)\n", "[1] \"Fetching average expression\"\n", "[1] \"Clusters Missing\"\n", "numeric(0)\n", "[1] \"Genes Missing\"\n", "character(0)\n", "[1] \"Adding missing clusters\"\n", " [1] \"1\" \"2\" \"3\" \"4\" \"5\" \"6\" \"7\" \"8\" \"9\" \"10\" \"11\" \"12\" \"13\"\n", "[1] \"Removing clusters because they're not represented in subset\"\n", "character(0)\n", "[1] \"Fetching average expression\"\n", "[1] \"Clusters Missing\"\n", "numeric(0)\n", "[1] \"Genes Missing\"\n", "character(0)\n", "[1] \"Adding missing clusters\"\n", " [1] \"1\" \"2\" \"3\" \"4\" \"5\" \"6\" \"7\" \"8\" \"9\" \"10\" \"11\" \"12\" \"13\"\n", "[1] \"Removing clusters because they're not represented in subset\"\n", "character(0)\n", " 0 1 2 3 4 5\n", "MT2A 1.5661375 1.7886136 1.23930550 -0.5550342 -0.65559979 1.0555638\n", "ISG15 0.4993533 0.5032622 0.34776935 -0.2345208 -0.36877340 1.0666874\n", "LY6E 1.3610672 1.7226326 0.71272245 0.5694496 0.46847515 1.3234267\n", "IFIT1 -0.8312661 -1.0030315 -0.99536271 -1.0284089 -1.06667170 -1.0091069\n", "IFIT2 -0.1833761 -0.5919883 -0.86381160 -0.9648878 -0.91581169 -0.8852175\n", "IFIT3 -0.5610933 -0.7005766 -0.80765375 -0.9734370 -0.91478731 -0.8504075\n", "IFITM1 0.7163677 1.1786239 -0.99111665 -0.7162096 -0.59417410 -0.3339678\n", "IFITM3 -0.7481583 0.6053509 1.36097315 -0.8413320 -0.91908497 2.0919832\n", "IFI44L -0.3655908 -0.1900213 -0.18308757 -0.5030346 -0.00596151 0.4112822\n", "IFI6 0.8078117 0.4568126 0.72563067 -0.4899924 -0.44855668 1.7102439\n", "MX1 -0.2781237 -0.3331252 -0.05474538 -0.2355774 -0.31991660 0.1428211\n", "IFI27 -0.6649793 -0.7945464 1.18836716 -0.9273441 -0.97302369 2.4824642\n", "IFI44L -0.3655908 -0.1900213 -0.18308757 -0.5030346 -0.00596151 0.4112822\n", "RSAD2 -0.8741876 -0.9431731 -0.94281399 -1.0394704 -0.99382746 -0.8929787\n", "SIGLEC1 -1.0898101 -1.0832983 -0.67332057 -1.0951121 -1.10090603 -0.5516686\n", "IFIT1 -0.8312661 -1.0030315 -0.99536271 -1.0284089 -1.06667170 -1.0091069\n", "ISG15 0.4993533 0.5032622 0.34776935 -0.2345208 -0.36877340 1.0666874\n", " 6 7 8 9 10\n", "MT2A 0.97112881 1.04449364 1.4730711 1.67963920 2.3769763060\n", "ISG15 0.49371473 -0.06766124 1.3454657 1.01148474 1.9780275478\n", "LY6E 0.82537227 0.56935813 1.4564183 1.63989078 1.8207186914\n", "IFIT1 -0.78641947 -1.06945012 -0.8808236 -0.67103267 -0.6271182416\n", "IFIT2 -0.30000076 -1.04620971 -0.8471594 0.02829051 -0.4258682077\n", "IFIT3 -0.49202288 -1.04970422 -0.7722362 -0.20614260 -0.3613099708\n", "IFITM1 -0.04861818 0.16215513 -0.6299032 0.93449458 -0.0006030242\n", "IFITM3 -0.09620276 -0.85133863 2.6302317 0.06148587 3.3737680356\n", "IFI44L 0.69996123 -0.57157157 0.4050462 -0.10998513 0.3574717770\n", "IFI6 0.58890745 -0.46663128 1.6700365 1.13217477 1.4391747926\n", "MX1 0.08308631 -0.42244581 0.1856889 -0.06906754 0.2247180152\n", "IFI27 0.21812419 -0.79457010 3.0687954 -0.39413760 3.2628479455\n", "IFI44L 0.69996123 -0.57157157 0.4050462 -0.10998513 0.3574717770\n", "RSAD2 -0.90765767 -0.96949322 -0.7359829 -0.85390356 -0.6003320774\n", "SIGLEC1 -1.02823105 -1.08230701 -0.2202100 -1.09212028 -0.5390421190\n", "IFIT1 -0.78641947 -1.06945012 -0.8808236 -0.67103267 -0.6271182416\n", "ISG15 0.49371473 -0.06766124 1.3454657 1.01148474 1.9780275478\n", " 11 12 13\n", "MT2A 2.097466434 0.9664404 -0.76250316\n", "ISG15 0.933059192 0.3062063 -0.29930243\n", "LY6E 1.613294209 0.5702544 -0.04408697\n", "IFIT1 -1.023063518 -1.0269426 -1.05318908\n", "IFIT2 -0.728207956 -0.9984917 0.20520728\n", "IFIT3 -0.705151271 -1.0285261 -1.10090603\n", "IFITM1 0.475586158 -0.8282103 -1.10090603\n", "IFITM3 -0.003438367 1.2922932 -1.00250815\n", "IFI44L -0.533376388 -0.4839937 0.24620164\n", "IFI6 0.692221634 0.4928043 -0.94717228\n", "MX1 -0.226044595 -0.1511075 0.77927552\n", "IFI27 -0.426874477 0.5216361 -0.17627378\n", "IFI44L -0.533376388 -0.4839937 0.24620164\n", "RSAD2 -0.999540734 -0.9713417 -1.02603820\n", "SIGLEC1 -1.099420827 -0.8498694 -1.10090603\n", "IFIT1 -1.023063518 -1.0269426 -1.05318908\n", "ISG15 0.933059192 0.3062063 -0.29930243\n", " 0 1 2 3 4 5\n", "MT2A 1.8530995 1.99499551 1.3119358 -0.5166546 -0.9670566 1.61898860\n", "ISG15 0.1251421 0.83842669 1.2324252 0.2730845 -0.3031859 2.61190061\n", "LY6E 1.0285434 1.91780528 1.4922022 0.9878010 0.7653291 2.09681656\n", "IFIT1 -1.0255083 -0.97429893 -0.8798111 -0.9368537 -1.0655900 -0.13094841\n", "IFIT2 -0.9112169 -0.81101730 -1.0394625 -0.8924834 -0.9756728 -1.10090603\n", "IFIT3 -0.9184662 -0.67535849 -0.7512549 -0.5048533 -0.8598418 -0.22740148\n", "IFITM1 1.0863665 1.87578962 -0.8880325 -0.6039801 -0.5513433 0.33598093\n", "IFITM3 -0.9087665 0.13116292 1.9372432 -1.0690673 -1.0910621 2.05556972\n", "IFI44L -0.5562783 0.36032784 0.7721069 0.5613439 0.7350153 0.03233568\n", "IFI6 0.1713111 0.76911827 1.6381911 -0.2510839 -0.1897512 2.17829247\n", "MX1 -0.3908565 -0.01363503 0.4279778 0.3628740 0.1526825 0.39153829\n", "IFI27 -0.9823968 -1.01402652 1.8946233 -0.9869349 -1.0606583 1.19082430\n", "IFI44L -0.5562783 0.36032784 0.7721069 0.5613439 0.7350153 0.03233568\n", "RSAD2 -0.9525591 -0.84824535 -0.8536787 -0.9317960 -0.8970531 0.05487563\n", "SIGLEC1 -1.1009060 -1.07852906 -0.1453908 -1.0920565 -1.1009060 -0.61803770\n", "IFIT1 -1.0255083 -0.97429893 -0.8798111 -0.9368537 -1.0655900 -0.13094841\n", "ISG15 0.1251421 0.83842669 1.2324252 0.2730845 -0.3031859 2.61190061\n", " 6 7 8 9 10 11\n", "MT2A 1.1627493 1.3991060 1.6344891 1.8611054 2.16485684 1.75149915\n", "ISG15 0.6589053 0.7239657 1.7204641 0.4388608 2.25401639 1.02160188\n", "LY6E 1.3512896 1.2705586 2.0283481 1.6213333 2.23067722 1.60968815\n", "IFIT1 -1.1009060 -0.9342397 -0.7546391 -1.1009060 -0.72658778 -0.86900639\n", "IFIT2 -0.8875537 -0.8997867 -0.8255108 -0.9146469 -0.04191050 -0.93152020\n", "IFIT3 -0.2496432 -0.7952523 -0.4755015 -0.7406219 0.26624962 -0.79420860\n", "IFITM1 0.4109916 1.0035372 -0.6420771 1.0243505 0.21465700 1.08899562\n", "IFITM3 -0.1772194 -0.8995874 2.7422740 -0.6401863 3.43016677 -0.43355627\n", "IFI44L 1.4049921 0.5154240 1.3346070 -0.4896273 0.98126225 0.04711047\n", "IFI6 -0.7869679 0.3807043 2.2345110 0.3410070 1.68177173 0.83368288\n", "MX1 -0.4416707 0.2741846 0.6822021 -0.1672204 0.37711327 0.20567245\n", "IFI27 -1.1009060 -1.0309345 2.5514802 -1.0087737 1.50000952 -0.80495969\n", "IFI44L 1.4049921 0.5154240 1.3346070 -0.4896273 0.98126225 0.04711047\n", "RSAD2 -0.5159162 -0.8530498 -0.4100307 -1.1009060 -0.61210323 -1.00085075\n", "SIGLEC1 -1.1009060 -1.0858491 0.2276062 -1.1009060 -0.05029528 -1.10090603\n", "IFIT1 -1.1009060 -0.9342397 -0.7546391 -1.1009060 -0.72658778 -0.86900639\n", "ISG15 0.6589053 0.7239657 1.7204641 0.4388608 2.25401639 1.02160188\n", " 12 13\n", "MT2A 1.1019627 -0.5781588\n", "ISG15 1.5019593 0.2205289\n", "LY6E 1.4376308 0.7312022\n", "IFIT1 -1.0248688 -1.1009060\n", "IFIT2 -0.9373676 -0.4116207\n", "IFIT3 -0.8369048 -1.0356245\n", "IFITM1 -0.2272543 -1.1009060\n", "IFITM3 1.8655567 -1.1009060\n", "IFI44L 0.5269852 1.6852446\n", "IFI6 1.1509812 -1.0503218\n", "MX1 0.5534834 1.7502121\n", "IFI27 1.2777615 0.7392393\n", "IFI44L 0.5269852 1.6852446\n", "RSAD2 -0.8802976 -1.0503218\n", "SIGLEC1 -0.7649117 -1.1009060\n", "IFIT1 -1.0248688 -1.1009060\n", "ISG15 1.5019593 0.2205289\n", " 0 1 2 3 4\n", "MT2A 1.47775456 1.59238092 1.08182126 -0.65247516 -0.747846009\n", "ISG15 0.10668894 1.02765285 0.87208855 0.10480283 -0.026842222\n", "LY6E 0.98955933 1.68502758 0.87110397 0.97617932 0.700786632\n", "IFIT1 -1.02614059 -0.97077811 -0.73759529 -0.96638673 -1.006074680\n", "IFIT2 -0.94039694 -0.86054001 -0.59642281 -0.91074401 -0.964026123\n", "IFIT3 -1.04919432 -0.71778771 -0.53285910 -0.51704136 -0.598247494\n", "IFITM1 0.27471415 1.16493748 -0.97449795 -0.58490495 -0.446340244\n", "IFITM3 -0.82563000 0.67654717 1.44580766 -0.98226566 -0.769659579\n", "IFI44L -0.73174649 0.14679088 0.15140933 -0.09067526 0.523334329\n", "IFI6 0.09579731 0.68382026 1.12507406 -0.43015907 -0.345050627\n", "MX1 -0.49068460 0.05488102 0.04964318 0.21816924 -0.001423449\n", "IFI27 -1.07601630 -0.97624860 -1.09202958 -1.05981865 -1.100906033\n", "IFI44L -0.73174649 0.14679088 0.15140933 -0.09067526 0.523334329\n", "RSAD2 -1.03001333 -0.82900344 -0.73236596 -1.03508505 -0.899932724\n", "SIGLEC1 -1.10090603 -1.09285709 -0.58627462 -1.10090603 -1.100906033\n", "IFIT1 -1.02614059 -0.97077811 -0.73759529 -0.96638673 -1.006074680\n", "ISG15 0.10668894 1.02765285 0.87208855 0.10480283 -0.026842222\n", " 5 6 7 8 9 10\n", "MT2A 1.14288390 0.96521167 0.6742512 1.5349394 1.65740771 2.34685922\n", "ISG15 0.07780871 0.03600680 0.1722583 1.6742448 0.23263696 1.93907495\n", "LY6E 1.03871470 0.62914373 0.8735038 1.6300197 1.41757672 2.28146987\n", "IFIT1 -1.03541718 -0.86733246 -1.0143018 -0.4482479 -0.95509971 -0.14707131\n", "IFIT2 -0.81666053 -0.69089888 -0.9592680 -0.2014677 -0.81078236 0.12817852\n", "IFIT3 -1.00928584 -0.67638812 -0.8765484 -0.1347016 -1.01321888 0.50846973\n", "IFITM1 -0.30604478 0.02147881 0.4294292 -0.6005878 0.84113677 0.09458812\n", "IFITM3 1.40113147 0.30689859 -0.7206809 2.5952731 -0.39015959 3.63317533\n", "IFI44L -0.06237858 0.20703600 -0.3535896 0.8298465 -0.66453866 0.43686248\n", "IFI6 0.60943131 0.03090142 0.2584836 1.7840499 0.05360894 1.47220197\n", "MX1 -0.06135862 -0.06505352 -0.3757631 0.6775134 -0.37470685 0.42510194\n", "IFI27 -0.90358796 -1.10090603 -0.9427276 -0.7294088 -1.10090603 -0.41519911\n", "IFI44L -0.06237858 0.20703600 -0.3535896 0.8298465 -0.66453866 0.43686248\n", "RSAD2 -0.81229952 -0.95038190 -0.8597831 -0.5286751 -0.95261003 -0.38734936\n", "SIGLEC1 -0.88477726 -1.10090603 -1.1009060 -0.1618870 -1.10090603 -0.95208442\n", "IFIT1 -1.03541718 -0.86733246 -1.0143018 -0.4482479 -0.95509971 -0.14707131\n", "ISG15 0.07780871 0.03600680 0.1722583 1.6742448 0.23263696 1.93907495\n", " 11 12 13\n", "MT2A 2.64278474 1.0225344 -0.79936868\n", "ISG15 0.28411584 0.9704662 0.65828548\n", "LY6E 1.54589841 0.9596978 -0.12786357\n", "IFIT1 -1.10090603 -0.6174901 -1.05156434\n", "IFIT2 -0.93191824 -0.8049464 -0.02961328\n", "IFIT3 -1.02090533 -0.5648470 -0.90300184\n", "IFITM1 -0.08883392 -0.7277134 -1.08320113\n", "IFITM3 0.40952051 1.4181671 -0.80575638\n", "IFI44L -0.97768753 -0.1643591 0.74478397\n", "IFI6 0.03557848 0.9484930 -0.93360268\n", "MX1 -0.54618431 0.1607998 0.97712908\n", "IFI27 -1.10090603 -1.0711488 -1.03714224\n", "IFI44L -0.97768753 -0.1643591 0.74478397\n", "RSAD2 -0.86361686 -0.8366124 -0.93652941\n", "SIGLEC1 -1.10090603 -0.6820895 -1.10090603\n", "IFIT1 -1.10090603 -0.6174901 -1.05156434\n", "ISG15 0.28411584 0.9704662 0.65828548\n", "[1] \"heatmap_split_isg_genes_ALL 30 10\"\n", "[1] \"Saving to file heatmap_split_isg_genes_ALL.png\"\n", "[1] \"Saving to file heatmap_split_isg_genes_ALL.pdf\"\n", "[1] \"Saving to file heatmap_split_isg_genes_ALL.svg\"\n" ] }, { "data": { "image/png": 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"text/plain": [ "plot without title" ] }, "metadata": { "image/png": { "height": 600, "width": 1800 } }, "output_type": "display_data" } ], "source": [ "\n", "p=makeComplexExprHeatmapSplit(obj.integrated, plotGOIs, group.by=\"idents\", split.by=\"condition\", include_all_clusters = TRUE, title=\"Heatmap of ISG Genes\", scale.by=\"ALL\")\n", "save_plot(p, \"heatmap_split_isg_genes_ALL\", 30, 10, save.data = FALSE, draw.fun=ComplexHeatmap::draw)\n", "\n" ] }, { "cell_type": "code", "execution_count": 33, "id": "ae9aca89", "metadata": { "fig.height": 10, "fig.width": 30, "lines_to_next_cell": 2 }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Scaling data GROUP[1] 0\n", " [1] \"MT2A\" \"ISG15\" \"LY6E\" \"IFIT1\" \"IFIT2\" \"IFIT3\" \"IFITM1\" \n", " [8] \"IFITM3\" \"IFI44L\" \"IFI6\" \"MX1\" \"IFI27\" \"IFI44L\" \"RSAD2\" \n", "[15] \"SIGLEC1\" \"IFIT1\" \"ISG15\" \n", "[1] \"Fetching average expression\"\n", "[1] \"Clusters Missing\"\n", "numeric(0)\n", "[1] \"Genes Missing\"\n", "character(0)\n", "[1] \"Adding missing clusters\"\n", " [1] \"1\" \"2\" \"3\" \"4\" \"5\" \"6\" \"7\" \"8\" \"9\" \"10\" \"11\" \"12\" \"13\"\n", "[1] \"Removing clusters because they're not represented in subset\"\n", "character(0)\n", "[1] \"Fetching average expression\"\n", "[1] \"Clusters Missing\"\n", "numeric(0)\n", "[1] \"Genes Missing\"\n", "character(0)\n", "[1] \"Adding missing clusters\"\n", " [1] \"1\" \"2\" \"3\" \"4\" \"5\" \"6\" \"7\" \"8\" \"9\" \"10\" \"11\" \"12\" \"13\"\n", "[1] \"Removing clusters because they're not represented in subset\"\n", "character(0)\n", "[1] \"Fetching average expression\"\n", "[1] \"Clusters Missing\"\n", "numeric(0)\n", "[1] \"Genes Missing\"\n", "character(0)\n", "[1] \"Adding missing clusters\"\n", " [1] \"1\" \"2\" \"3\" \"4\" \"5\" \"6\" \"7\" \"8\" \"9\" \"10\" \"11\" \"12\" \"13\"\n", "[1] \"Removing clusters because they're not represented in subset\"\n", "character(0)\n", "[1] \"heatmap_split_isg_genes_GROUP 30 10\"\n", "[1] \"Saving to file heatmap_split_isg_genes_GROUP.png\"\n", "[1] \"Saving to file heatmap_split_isg_genes_GROUP.pdf\"\n", "[1] \"Saving to file heatmap_split_isg_genes_GROUP.svg\"\n" ] }, { "data": { "image/png": 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"text/plain": [ "plot without title" ] }, "metadata": { "image/png": { "height": 600, "width": 1800 } }, "output_type": "display_data" } ], "source": [ "\n", "\n", "p=makeComplexExprHeatmapSplit(obj.integrated, plotGOIs, group.by=\"idents\", split.by=\"condition\", include_all_clusters = TRUE, title=\"Heatmap of ISG Genes\", scale.by=\"GROUP\")\n", "save_plot(p, \"heatmap_split_isg_genes_GROUP\", 30, 10, save.data = FALSE, draw.fun=ComplexHeatmap::draw)\n", "\n" ] }, { "cell_type": "code", "execution_count": 34, "id": "fc5d093b", "metadata": { "fig.height": 12, "fig.width": 16 }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[1] \"SCALING BY ALL\"\n", " Var1 Freq perc\n", "1 0 309 0.02106052\n", "2 1 699 0.04764177\n", "3 2 741 0.05050436\n", "4 3 188 0.01281352\n", "5 4 338 0.02303708\n", "6 5 274 0.01867503\n", " Var1 Freq perc\n", "1 0 2659 0.18122955\n", "2 1 1176 0.08015267\n", "3 2 558 0.03803162\n", "4 3 781 0.05323064\n", "5 4 442 0.03012541\n", "6 5 615 0.04191658\n", " Var1 Freq perc\n", "1 0 444 0.030261723\n", "2 1 642 0.043756816\n", "3 2 100 0.006815703\n", "4 3 254 0.017311887\n", "5 4 161 0.010973282\n", "6 5 40 0.002726281\n", "[1] \"Final plot checks\"\n", "[1] \"descr label\"\n", "[1] \"descr label\"\n", "[1] \"descr label\"\n", "[1] \"Preparing Legend\"\n", "[1] \"Preparing plot\"\n", "[1] \"edotplot_split_isg_genes_ALL 30 10\"\n", "[1] \"Saving to file edotplot_split_isg_genes_ALL.png\"\n", "[1] \"Saving to file edotplot_split_isg_genes_ALL.pdf\"\n", "[1] \"Saving to file edotplot_split_isg_genes_ALL.svg\"\n" ] }, { "data": { "image/png": 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"text/plain": [ "plot without title" ] }, "metadata": { "image/png": { "height": 600, "width": 1800 } }, "output_type": "display_data" } ], "source": [ "\n", "cells.control = cellIDForClusters(obj.integrated, \"condition\", c(\"CONTROL\"))\n", "cells.symptomatic = cellIDForClusters(obj.integrated, \"condition\", c(\"SYMPTOMATIC\"))\n", "cells.asymptomatic = cellIDForClusters(obj.integrated, \"condition\", c(\"ASYMPTOMATIC\"))\n", "\n", "plotElems = list()\n", "plotElems[[\"Control\"]] = list(cells=intersect(cells.control, cells.control), label=\"Control\")\n", "plotElems[[\"Symptomatic\"]] = list(cells=intersect(cells.symptomatic,cells.symptomatic), label=\"Symptomatic\")\n", "plotElems[[\"Asymptomatic\"]] = list(cells=intersect(cells.asymptomatic,cells.asymptomatic), label=\"Asymptomatic\")\n", "\n", "p=enhancedDotPlot(obj.integrated, plotElems, featureGenes = isgGenes, group.by=\"idents\", title=\"DotPlot of ISG Genes\", scale.by=\"ALL\", rotate.x = T)\n", "save_plot(p, \"edotplot_split_isg_genes_ALL\", 30, 10, save.data = FALSE)\n", "\n" ] }, { "cell_type": "code", "execution_count": 35, "id": "22cc6ed9", "metadata": { "fig.height": 12, "fig.width": 16, "lines_to_next_cell": 2 }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[1] \"SCALING BY GLOBAL\"\n", " Var1 Freq perc\n", "1 0 309 0.02106052\n", "2 1 699 0.04764177\n", "3 2 741 0.05050436\n", "4 3 188 0.01281352\n", "5 4 338 0.02303708\n", "6 5 274 0.01867503\n", " Var1 Freq perc\n", "1 0 2659 0.18122955\n", "2 1 1176 0.08015267\n", "3 2 558 0.03803162\n", "4 3 781 0.05323064\n", "5 4 442 0.03012541\n", "6 5 615 0.04191658\n", " Var1 Freq perc\n", "1 0 444 0.030261723\n", "2 1 642 0.043756816\n", "3 2 100 0.006815703\n", "4 3 254 0.017311887\n", "5 4 161 0.010973282\n", "6 5 40 0.002726281\n", "[1] \"Final plot checks\"\n", "[1] \"descr label\"\n", "[1] \"descr label\"\n", "[1] \"descr label\"\n", "[1] \"Preparing Legend\"\n", "[1] \"Preparing plot\"\n", "[1] \"edotplot_split_isg_genes_GLOBAL 30 10\"\n", "[1] \"Saving to file edotplot_split_isg_genes_GLOBAL.png\"\n", "[1] \"Saving to file edotplot_split_isg_genes_GLOBAL.pdf\"\n", "[1] \"Saving to file edotplot_split_isg_genes_GLOBAL.svg\"\n" ] }, { "data": { "image/png": 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"text/plain": [ "plot without title" ] }, "metadata": { "image/png": { "height": 600, "width": 1800 } }, "output_type": "display_data" } ], "source": [ "\n", "p=enhancedDotPlot(obj.integrated, plotElems, featureGenes = isgGenes, group.by=\"idents\", title=\"DotPlot of ISG Genes\", scale.by=\"GLOBAL\", rotate.x = T)\n", "save_plot(p, \"edotplot_split_isg_genes_GLOBAL\", 30, 10, save.data = FALSE)\n", "\n" ] }, { "cell_type": "code", "execution_count": 36, "id": "9c24aec8", "metadata": { "fig.height": 12, "fig.width": 16 }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[1] \"SCALING BY GROUP\"\n", " Var1 Freq perc\n", "1 0 309 0.02106052\n", "2 1 699 0.04764177\n", "3 2 741 0.05050436\n", "4 3 188 0.01281352\n", "5 4 338 0.02303708\n", "6 5 274 0.01867503\n", " Var1 Freq perc\n", "1 0 2659 0.18122955\n", "2 1 1176 0.08015267\n", "3 2 558 0.03803162\n", "4 3 781 0.05323064\n", "5 4 442 0.03012541\n", "6 5 615 0.04191658\n", " Var1 Freq perc\n", "1 0 444 0.030261723\n", "2 1 642 0.043756816\n", "3 2 100 0.006815703\n", "4 3 254 0.017311887\n", "5 4 161 0.010973282\n", "6 5 40 0.002726281\n", "[1] \"Final plot checks\"\n", "[1] \"descr label\"\n", "[1] \"descr label\"\n", "[1] \"descr label\"\n", "[1] \"Preparing Legend\"\n", "[1] \"Preparing plot\"\n", "[1] \"edotplot_split_isg_genes_GROUP 30 10\"\n", "[1] \"Saving to file edotplot_split_isg_genes_GROUP.png\"\n", "[1] \"Saving to file edotplot_split_isg_genes_GROUP.pdf\"\n", "[1] \"Saving to file edotplot_split_isg_genes_GROUP.svg\"\n" ] }, { "data": { "image/png": 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"text/plain": [ "plot without title" ] }, "metadata": { "image/png": { "height": 600, "width": 1800 } }, "output_type": "display_data" } ], "source": [ "\n", "p=enhancedDotPlot(obj.integrated, plotElems, featureGenes = isgGenes, group.by=\"idents\", title=\"DotPlot of ISG Genes\", scale.by=\"GROUP\",rotate.x = T)\n", "save_plot(p, \"edotplot_split_isg_genes_GROUP\", 30, 10, save.data = FALSE)\n", "\n" ] }, { "cell_type": "code", "execution_count": 37, "id": "ac52eab5", "metadata": { "fig.height": 12, "fig.width": 16, "lines_to_next_cell": 0 }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[1] \"SCALING BY FEATURE\"\n", " [1] \"IFI27\" \"IFI44L\" \"IFI6\" \"IFIT1\" \"IFIT2\" \"IFIT3\" \"IFITM1\" \n", " [8] \"IFITM3\" \"ISG15\" \"LY6E\" \"MT2A\" \"MX1\" \"RSAD2\" \"SIGLEC1\"\n", "[1] \"IFI27\"\n", "[1] \"IFI44L\"\n", "[1] \"IFI6\"\n", "[1] \"IFIT1\"\n", "[1] \"IFIT2\"\n", "[1] \"IFIT3\"\n", "[1] \"IFITM1\"\n", "[1] \"IFITM3\"\n", "[1] \"ISG15\"\n", "[1] \"LY6E\"\n", "[1] \"MT2A\"\n", "[1] \"MX1\"\n", "[1] \"RSAD2\"\n", "[1] \"SIGLEC1\"\n", " Var1 Freq perc\n", "1 0 309 0.02106052\n", "2 1 699 0.04764177\n", "3 2 741 0.05050436\n", "4 3 188 0.01281352\n", "5 4 338 0.02303708\n", "6 5 274 0.01867503\n", " Var1 Freq perc\n", "1 0 2659 0.18122955\n", "2 1 1176 0.08015267\n", "3 2 558 0.03803162\n", "4 3 781 0.05323064\n", "5 4 442 0.03012541\n", "6 5 615 0.04191658\n", " Var1 Freq perc\n", "1 0 444 0.030261723\n", "2 1 642 0.043756816\n", "3 2 100 0.006815703\n", "4 3 254 0.017311887\n", "5 4 161 0.010973282\n", "6 5 40 0.002726281\n", "[1] \"Final plot checks\"\n", "[1] \"descr label\"\n", "[1] \"descr label\"\n", "[1] \"descr label\"\n", "[1] \"Preparing Legend\"\n", "[1] \"Preparing plot\"\n", "[1] \"edotplot_split_isg_genes_FEATURE 30 10\"\n", "[1] \"Saving to file edotplot_split_isg_genes_FEATURE.png\"\n", "[1] \"Saving to file edotplot_split_isg_genes_FEATURE.pdf\"\n", "[1] \"Saving to file edotplot_split_isg_genes_FEATURE.svg\"\n" ] }, { "data": { "image/png": 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"text/plain": [ "plot without title" ] }, "metadata": { "image/png": { "height": 600, "width": 1800 } }, "output_type": "display_data" } ], "source": [ "\n", "p=enhancedDotPlot(obj.integrated, plotElems, featureGenes = isgGenes, group.by=\"idents\", title=\"DotPlot of ISG Genes\", scale.by=\"FEATURE\", rotate.x=TRUE)\n", "save_plot(p, \"edotplot_split_isg_genes_FEATURE\", 30, 10, save.data = FALSE)\n", "\n" ] }, { "cell_type": "code", "execution_count": 38, "id": "bb0736bc-4a29-472d-a34f-a27167d5d991", "metadata": {}, "outputs": [], "source": [ "options(repr.plot.width = 16, repr.plot.height = 12)" ] }, { "cell_type": "markdown", "id": "111999e9", "metadata": {}, "source": [ "\n" ] }, { "cell_type": "code", "execution_count": 39, "id": "54514f82", "metadata": { "lines_to_next_cell": 0 }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[1] \"Processing cluster 0 with a total of 3412 cells\"\n", "[1] 851 120\n", "[1] \"./de\"\n", "[1] \"Dir already exists!\"\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "./de/cluster_0.symptomatic_asymptomatic.tsv\n", "\n", "./de/cluster_0.symptomatic_asymptomatic.xlsx\n", "\n", "Centering and scaling data matrix\n", "\n", "\u001b[1m\u001b[22mScale for \u001b[32mfill\u001b[39m is already present.\n", "Adding another scale for \u001b[32mfill\u001b[39m, which will replace the existing scale.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[1] \"./de/hplot_cluster_0.symptomatic_asymptomatic 7 12\"\n", "[1] \"Saving to file ./de/hplot_cluster_0.symptomatic_asymptomatic.png\"\n", "[1] \"Saving to file ./de/hplot_cluster_0.symptomatic_asymptomatic.pdf\"\n", "[1] \"Saving to file ./de/hplot_cluster_0.symptomatic_asymptomatic.svg\"\n", "[1] \"Saving to file ./de/hplot_cluster_0.symptomatic_asymptomatic.data\"\n", "[1] \"Processing cluster 1 with a total of 2517 cells\"\n", "[1] 343 223\n", "[1] \"./de\"\n", "[1] \"Dir already exists!\"\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "./de/cluster_1.symptomatic_asymptomatic.tsv\n", "\n", "./de/cluster_1.symptomatic_asymptomatic.xlsx\n", "\n", "Centering and scaling data matrix\n", "\n", "\u001b[1m\u001b[22mScale for \u001b[32mfill\u001b[39m is already present.\n", "Adding another scale for \u001b[32mfill\u001b[39m, which will replace the existing scale.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[1] \"./de/hplot_cluster_1.symptomatic_asymptomatic 7 12\"\n", "[1] \"Saving to file ./de/hplot_cluster_1.symptomatic_asymptomatic.png\"\n", "[1] \"Saving to file ./de/hplot_cluster_1.symptomatic_asymptomatic.pdf\"\n", "[1] \"Saving to file ./de/hplot_cluster_1.symptomatic_asymptomatic.svg\"\n", "[1] \"Saving to file ./de/hplot_cluster_1.symptomatic_asymptomatic.data\"\n", "[1] \"Processing cluster 2 with a total of 1399 cells\"\n", "[1] 89 40\n", "[1] \"./de\"\n", "[1] \"Dir already exists!\"\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "./de/cluster_2.symptomatic_asymptomatic.tsv\n", "\n", "./de/cluster_2.symptomatic_asymptomatic.xlsx\n", "\n", "Centering and scaling data matrix\n", "\n", "\u001b[1m\u001b[22mScale for \u001b[32mfill\u001b[39m is already present.\n", "Adding another scale for \u001b[32mfill\u001b[39m, which will replace the existing scale.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[1] \"./de/hplot_cluster_2.symptomatic_asymptomatic 7 4.8\"\n", "[1] \"Saving to file ./de/hplot_cluster_2.symptomatic_asymptomatic.png\"\n", "[1] \"Saving to file ./de/hplot_cluster_2.symptomatic_asymptomatic.pdf\"\n", "[1] \"Saving to file ./de/hplot_cluster_2.symptomatic_asymptomatic.svg\"\n", "[1] \"Saving to file ./de/hplot_cluster_2.symptomatic_asymptomatic.data\"\n", "[1] \"Processing cluster 3 with a total of 1223 cells\"\n", "[1] 307 127\n", "[1] \"./de\"\n", "[1] \"Dir already exists!\"\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "./de/cluster_3.symptomatic_asymptomatic.tsv\n", "\n", "./de/cluster_3.symptomatic_asymptomatic.xlsx\n", "\n", "Centering and scaling data matrix\n", "\n", "\u001b[1m\u001b[22mScale for \u001b[32mfill\u001b[39m is already present.\n", "Adding another scale for \u001b[32mfill\u001b[39m, which will replace the existing scale.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[1] \"./de/hplot_cluster_3.symptomatic_asymptomatic 7 12\"\n", "[1] \"Saving to file ./de/hplot_cluster_3.symptomatic_asymptomatic.png\"\n", "[1] \"Saving to file ./de/hplot_cluster_3.symptomatic_asymptomatic.pdf\"\n", "[1] \"Saving to file ./de/hplot_cluster_3.symptomatic_asymptomatic.svg\"\n", "[1] \"Saving to file ./de/hplot_cluster_3.symptomatic_asymptomatic.data\"\n", "[1] \"Processing cluster 4 with a total of 941 cells\"\n", "[1] 178 61\n", "[1] \"./de\"\n", "[1] \"Dir already exists!\"\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "./de/cluster_4.symptomatic_asymptomatic.tsv\n", "\n", "./de/cluster_4.symptomatic_asymptomatic.xlsx\n", "\n", "Centering and scaling data matrix\n", "\n", "\u001b[1m\u001b[22mScale for \u001b[32mfill\u001b[39m is already present.\n", "Adding another scale for \u001b[32mfill\u001b[39m, which will replace the existing scale.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[1] \"./de/hplot_cluster_4.symptomatic_asymptomatic 7 10.2\"\n", "[1] \"Saving to file ./de/hplot_cluster_4.symptomatic_asymptomatic.png\"\n", "[1] \"Saving to file ./de/hplot_cluster_4.symptomatic_asymptomatic.pdf\"\n", "[1] \"Saving to file ./de/hplot_cluster_4.symptomatic_asymptomatic.svg\"\n", "[1] \"Saving to file ./de/hplot_cluster_4.symptomatic_asymptomatic.data\"\n", "[1] \"Processing cluster 5 with a total of 929 cells\"\n", "[1] 249 30\n", "[1] \"./de\"\n", "[1] \"Dir already exists!\"\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "./de/cluster_5.symptomatic_asymptomatic.tsv\n", "\n", "./de/cluster_5.symptomatic_asymptomatic.xlsx\n", "\n", "Centering and scaling data matrix\n", "\n", "\u001b[1m\u001b[22mScale for \u001b[32mfill\u001b[39m is already present.\n", "Adding another scale for \u001b[32mfill\u001b[39m, which will replace the existing scale.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[1] \"./de/hplot_cluster_5.symptomatic_asymptomatic 7 12\"\n", "[1] \"Saving to file ./de/hplot_cluster_5.symptomatic_asymptomatic.png\"\n", "[1] \"Saving to file ./de/hplot_cluster_5.symptomatic_asymptomatic.pdf\"\n", "[1] \"Saving to file ./de/hplot_cluster_5.symptomatic_asymptomatic.svg\"\n", "[1] \"Saving to file ./de/hplot_cluster_5.symptomatic_asymptomatic.data\"\n", "[1] \"Processing cluster 6 with a total of 910 cells\"\n", "[1] 394 35\n", "[1] \"./de\"\n", "[1] \"Dir already exists!\"\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "./de/cluster_6.symptomatic_asymptomatic.tsv\n", "\n", "./de/cluster_6.symptomatic_asymptomatic.xlsx\n", "\n", "Centering and scaling data matrix\n", "\n", "\u001b[1m\u001b[22mScale for \u001b[32mfill\u001b[39m is already present.\n", "Adding another scale for \u001b[32mfill\u001b[39m, which will replace the existing scale.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[1] \"./de/hplot_cluster_6.symptomatic_asymptomatic 7 12\"\n", "[1] \"Saving to file ./de/hplot_cluster_6.symptomatic_asymptomatic.png\"\n", "[1] \"Saving to file ./de/hplot_cluster_6.symptomatic_asymptomatic.pdf\"\n", "[1] \"Saving to file ./de/hplot_cluster_6.symptomatic_asymptomatic.svg\"\n", "[1] \"Saving to file ./de/hplot_cluster_6.symptomatic_asymptomatic.data\"\n", "[1] \"Processing cluster 7 with a total of 889 cells\"\n", "[1] 104 129\n", "[1] \"./de\"\n", "[1] \"Dir already exists!\"\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "./de/cluster_7.symptomatic_asymptomatic.tsv\n", "\n", "./de/cluster_7.symptomatic_asymptomatic.xlsx\n", "\n", "Centering and scaling data matrix\n", "\n", "\u001b[1m\u001b[22mScale for \u001b[32mfill\u001b[39m is already present.\n", "Adding another scale for \u001b[32mfill\u001b[39m, which will replace the existing scale.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[1] \"./de/hplot_cluster_7.symptomatic_asymptomatic 7 2.7\"\n", "[1] \"Saving to file ./de/hplot_cluster_7.symptomatic_asymptomatic.png\"\n", "[1] \"Saving to file ./de/hplot_cluster_7.symptomatic_asymptomatic.pdf\"\n", "[1] \"Saving to file ./de/hplot_cluster_7.symptomatic_asymptomatic.svg\"\n", "[1] \"Saving to file ./de/hplot_cluster_7.symptomatic_asymptomatic.data\"\n", "[1] \"Processing cluster 8 with a total of 836 cells\"\n", "[1] 158 59\n", "[1] \"./de\"\n", "[1] \"Dir already exists!\"\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "./de/cluster_8.symptomatic_asymptomatic.tsv\n", "\n", "./de/cluster_8.symptomatic_asymptomatic.xlsx\n", "\n", "Centering and scaling data matrix\n", "\n", "\u001b[1m\u001b[22mScale for \u001b[32mfill\u001b[39m is already present.\n", "Adding another scale for \u001b[32mfill\u001b[39m, which will replace the existing scale.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[1] \"./de/hplot_cluster_8.symptomatic_asymptomatic 7 12\"\n", "[1] \"Saving to file ./de/hplot_cluster_8.symptomatic_asymptomatic.png\"\n", "[1] \"Saving to file ./de/hplot_cluster_8.symptomatic_asymptomatic.pdf\"\n", "[1] \"Saving to file ./de/hplot_cluster_8.symptomatic_asymptomatic.svg\"\n", "[1] \"Saving to file ./de/hplot_cluster_8.symptomatic_asymptomatic.data\"\n", "[1] \"Processing cluster 9 with a total of 656 cells\"\n", "[1] 195 9\n", "[1] \"./de\"\n", "[1] \"Dir already exists!\"\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "./de/cluster_9.symptomatic_asymptomatic.tsv\n", "\n", "./de/cluster_9.symptomatic_asymptomatic.xlsx\n", "\n", "Centering and scaling data matrix\n", "\n", "\u001b[1m\u001b[22mScale for \u001b[32mfill\u001b[39m is already present.\n", "Adding another scale for \u001b[32mfill\u001b[39m, which will replace the existing scale.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[1] \"./de/hplot_cluster_9.symptomatic_asymptomatic 7 12\"\n", "[1] \"Saving to file ./de/hplot_cluster_9.symptomatic_asymptomatic.png\"\n", "[1] \"Saving to file ./de/hplot_cluster_9.symptomatic_asymptomatic.pdf\"\n", "[1] \"Saving to file ./de/hplot_cluster_9.symptomatic_asymptomatic.svg\"\n", "[1] \"Saving to file ./de/hplot_cluster_9.symptomatic_asymptomatic.data\"\n", "[1] \"Processing cluster 10 with a total of 357 cells\"\n", "[1] 46 10\n", "[1] \"./de\"\n", "[1] \"Dir already exists!\"\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "./de/cluster_10.symptomatic_asymptomatic.tsv\n", "\n", "./de/cluster_10.symptomatic_asymptomatic.xlsx\n", "\n", "Centering and scaling data matrix\n", "\n", "\u001b[1m\u001b[22mScale for \u001b[32mfill\u001b[39m is already present.\n", "Adding another scale for \u001b[32mfill\u001b[39m, which will replace the existing scale.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[1] \"./de/hplot_cluster_10.symptomatic_asymptomatic 7 4.8\"\n", "[1] \"Saving to file ./de/hplot_cluster_10.symptomatic_asymptomatic.png\"\n", "[1] \"Saving to file ./de/hplot_cluster_10.symptomatic_asymptomatic.pdf\"\n", "[1] \"Saving to file ./de/hplot_cluster_10.symptomatic_asymptomatic.svg\"\n", "[1] \"Saving to file ./de/hplot_cluster_10.symptomatic_asymptomatic.data\"\n", "[1] \"Processing cluster 11 with a total of 349 cells\"\n", "[1] 79 28\n", "[1] \"./de\"\n", "[1] \"Dir already exists!\"\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "./de/cluster_11.symptomatic_asymptomatic.tsv\n", "\n", "./de/cluster_11.symptomatic_asymptomatic.xlsx\n", "\n", "Centering and scaling data matrix\n", "\n", "\u001b[1m\u001b[22mScale for \u001b[32mfill\u001b[39m is already present.\n", "Adding another scale for \u001b[32mfill\u001b[39m, which will replace the existing scale.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[1] \"./de/hplot_cluster_11.symptomatic_asymptomatic 7 11.4\"\n", "[1] \"Saving to file ./de/hplot_cluster_11.symptomatic_asymptomatic.png\"\n", "[1] \"Saving to file ./de/hplot_cluster_11.symptomatic_asymptomatic.pdf\"\n", "[1] \"Saving to file ./de/hplot_cluster_11.symptomatic_asymptomatic.svg\"\n", "[1] \"Saving to file ./de/hplot_cluster_11.symptomatic_asymptomatic.data\"\n", "[1] \"Processing cluster 12 with a total of 191 cells\"\n", "[1] 26 8\n", "[1] \"./de\"\n", "[1] \"Dir already exists!\"\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "./de/cluster_12.symptomatic_asymptomatic.tsv\n", "\n", "./de/cluster_12.symptomatic_asymptomatic.xlsx\n", "\n", "Centering and scaling data matrix\n", "\n", "\u001b[1m\u001b[22mScale for \u001b[32mfill\u001b[39m is already present.\n", "Adding another scale for \u001b[32mfill\u001b[39m, which will replace the existing scale.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[1] \"./de/hplot_cluster_12.symptomatic_asymptomatic 7 1.5\"\n", "[1] \"Saving to file ./de/hplot_cluster_12.symptomatic_asymptomatic.png\"\n", "[1] \"Saving to file ./de/hplot_cluster_12.symptomatic_asymptomatic.pdf\"\n", "[1] \"Saving to file ./de/hplot_cluster_12.symptomatic_asymptomatic.svg\"\n", "[1] \"Saving to file ./de/hplot_cluster_12.symptomatic_asymptomatic.data\"\n", "[1] \"Processing cluster 13 with a total of 63 cells\"\n", "[1] 5 5\n", "[1] \"./de\"\n", "[1] \"Dir already exists!\"\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "./de/cluster_13.symptomatic_asymptomatic.tsv\n", "\n", "./de/cluster_13.symptomatic_asymptomatic.xlsx\n", "\n" ] } ], "source": [ "\n", "cells.tp1 = cellIDForClusters(obj.integrated, \"tp\", c(\"TP 1\"))\n", "\n", "comparison.sympt_asympt = compareCellsByCluster(obj.integrated, intersect(cells.symptomatic, cells.tp1), intersect(cells.asymptomatic, cells.tp1), \"symptomatic\", \"asymptomatic\", outfolder=\"./de\", group.by=\"idents\", heatmap.plot=TRUE)\n" ] }, { "cell_type": "markdown", "id": "125fe551", "metadata": {}, "source": [ "\n" ] }, { "cell_type": "code", "execution_count": 40, "id": "b5628775", "metadata": { "lines_to_next_cell": 0 }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[1] \"./de_volcanos\"\n", "[1] \"Dir already exists!\"\n", "[1] \"0\"\n", "[1] 999 22\n", "[1] \"./de_volcanos/sympt_asympt_tp1.0.png\"\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Warning message:\n", "\"ggrepel: 17 unlabeled data points (too many overlaps). Consider increasing max.overlaps\"\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[1] \"./de_volcanos/sympt_asympt_tp1.0.pdf\"\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Warning message:\n", "\"ggrepel: 63 unlabeled data points (too many overlaps). Consider increasing max.overlaps\"\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[1] \"./de_volcanos/sympt_asympt_tp1.0.svg\"\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Warning message:\n", "\"ggrepel: 64 unlabeled data points (too many overlaps). Consider increasing max.overlaps\"\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[1] \"1\"\n", "[1] 554 22\n", "[1] \"./de_volcanos/sympt_asympt_tp1.1.png\"\n", "[1] \"./de_volcanos/sympt_asympt_tp1.1.pdf\"\n", "[1] \"./de_volcanos/sympt_asympt_tp1.1.svg\"\n", "[1] \"2\"\n", "[1] 1051 22\n", "[1] \"./de_volcanos/sympt_asympt_tp1.2.png\"\n", "[1] \"./de_volcanos/sympt_asympt_tp1.2.pdf\"\n", "[1] \"./de_volcanos/sympt_asympt_tp1.2.svg\"\n", "[1] \"3\"\n", "[1] 962 22\n", "[1] \"./de_volcanos/sympt_asympt_tp1.3.png\"\n", "[1] \"./de_volcanos/sympt_asympt_tp1.3.pdf\"\n", "[1] \"./de_volcanos/sympt_asympt_tp1.3.svg\"\n", "[1] \"4\"\n", "[1] 1248 22\n", "[1] \"./de_volcanos/sympt_asympt_tp1.4.png\"\n", "[1] \"./de_volcanos/sympt_asympt_tp1.4.pdf\"\n", "[1] \"./de_volcanos/sympt_asympt_tp1.4.svg\"\n", "[1] \"5\"\n", "[1] 1759 22\n", "[1] \"./de_volcanos/sympt_asympt_tp1.5.png\"\n", "[1] \"./de_volcanos/sympt_asympt_tp1.5.pdf\"\n", "[1] \"./de_volcanos/sympt_asympt_tp1.5.svg\"\n", "[1] \"6\"\n", "[1] 1473 22\n", "[1] \"./de_volcanos/sympt_asympt_tp1.6.png\"\n", "[1] \"./de_volcanos/sympt_asympt_tp1.6.pdf\"\n", "[1] \"./de_volcanos/sympt_asympt_tp1.6.svg\"\n", "[1] \"7\"\n", "[1] 1439 22\n", "[1] \"./de_volcanos/sympt_asympt_tp1.7.png\"\n", "[1] \"./de_volcanos/sympt_asympt_tp1.7.pdf\"\n", "[1] \"./de_volcanos/sympt_asympt_tp1.7.svg\"\n", "[1] \"8\"\n", "[1] 1421 22\n", "[1] \"./de_volcanos/sympt_asympt_tp1.8.png\"\n", "[1] \"./de_volcanos/sympt_asympt_tp1.8.pdf\"\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Warning message:\n", "\"ggrepel: 80 unlabeled data points (too many overlaps). Consider increasing max.overlaps\"\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[1] \"./de_volcanos/sympt_asympt_tp1.8.svg\"\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Warning message:\n", "\"ggrepel: 81 unlabeled data points (too many overlaps). Consider increasing max.overlaps\"\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[1] \"9\"\n", "[1] 4092 22\n", "[1] \"./de_volcanos/sympt_asympt_tp1.9.png\"\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Warning message:\n", "\"ggrepel: 754 unlabeled data points (too many overlaps). Consider increasing max.overlaps\"\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[1] \"./de_volcanos/sympt_asympt_tp1.9.pdf\"\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Warning message:\n", "\"ggrepel: 794 unlabeled data points (too many overlaps). Consider increasing max.overlaps\"\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[1] \"./de_volcanos/sympt_asympt_tp1.9.svg\"\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Warning message:\n", "\"ggrepel: 795 unlabeled data points (too many overlaps). Consider increasing max.overlaps\"\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[1] \"10\"\n", "[1] 3863 22\n", "[1] \"./de_volcanos/sympt_asympt_tp1.10.png\"\n", "[1] \"./de_volcanos/sympt_asympt_tp1.10.pdf\"\n", "[1] \"./de_volcanos/sympt_asympt_tp1.10.svg\"\n", "[1] \"11\"\n", "[1] 3481 22\n", "[1] \"./de_volcanos/sympt_asympt_tp1.11.png\"\n", "[1] \"./de_volcanos/sympt_asympt_tp1.11.pdf\"\n", "[1] \"./de_volcanos/sympt_asympt_tp1.11.svg\"\n", "[1] \"12\"\n", "[1] 2705 22\n", "[1] \"./de_volcanos/sympt_asympt_tp1.12.png\"\n", "[1] \"./de_volcanos/sympt_asympt_tp1.12.pdf\"\n", "[1] \"./de_volcanos/sympt_asympt_tp1.12.svg\"\n", "[1] \"13\"\n", "[1] 4768 22\n", "[1] \"./de_volcanos/sympt_asympt_tp1.13.png\"\n", "[1] \"./de_volcanos/sympt_asympt_tp1.13.pdf\"\n", "[1] \"./de_volcanos/sympt_asympt_tp1.13.svg\"\n" ] } ], "source": [ "\n", "makeVolcanos(comparison.sympt_asympt, \"Comparison Symptomatic vs. Asymptoamtic\", \"./de_volcanos/sympt_asympt_tp1\", FCcutoff=0.25)\n" ] }, { "cell_type": "markdown", "id": "e901d082", "metadata": {}, "source": [ "\n" ] }, { "cell_type": "code", "execution_count": 41, "id": "9983be81", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[1] \"Human Org DB\"\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "\n", "\n", "\n", "\n", "'select()' returned 1:many mapping between keys and columns\n", "\n", "Warning message in clusterProfiler::bitr(universeSym, fromType = \"SYMBOL\", toType = \"ENTREZID\", :\n", "\"35.98% of input gene IDs are fail to map...\"\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[1] \"0\"\n", "[1] \"Selecting Genes\"\n", " gene p_val avg_log2FC pct.1 pct.2 p_val_adj num.symptomatic\n", "2181 ACAP1 2.404475e-07 -0.5607755 0.454 0.683 8.800620e-03 386\n", "2312 ADGRG1 9.917797e-08 0.6660579 0.615 0.425 3.630013e-03 523\n", "2443 AK6 1.002442e-10 0.3335989 0.137 0.025 3.669039e-06 117\n", "3491 AOAH 1.290840e-07 0.5226751 0.355 0.167 4.724602e-03 302\n", "3853 ARL4C 6.216770e-07 0.4597286 0.985 0.967 2.275400e-02 838\n", "4073 ATP6V1E1 1.161034e-06 0.4265228 0.321 0.158 4.249499e-02 273\n", " min.symptomatic lower_hinge.symptomatic median.symptomatic\n", "2181 0.6037215 0.9797888 1.173145\n", "2312 0.6454174 1.0973478 1.475376\n", "2443 0.6037215 0.9815590 1.084805\n", "3491 0.5217806 1.0008444 1.168544\n", "3853 0.8603849 2.3353147 2.704539\n", "4073 0.5217806 0.9694118 1.087797\n", " upper_hinge.symptomatic max.symptomatic mean.symptomatic anum.symptomatic\n", "2181 1.521782 2.731604 1.273027 851\n", "2312 1.802147 2.898007 1.514921 851\n", "2443 1.304935 2.195094 1.145239 851\n", "3491 1.438998 2.608729 1.260579 851\n", "3853 3.015161 4.134571 2.648831 851\n", "4073 1.316409 2.547769 1.164952 851\n", " num.asymptomatic min.asymptomatic lower_hinge.asymptomatic\n", "2181 82 0.7540631 1.0034633\n", "2312 51 0.7540631 0.9962486\n", "2443 3 0.9029708 0.9175911\n", "3491 20 0.9014036 0.9854102\n", "3853 116 0.9063864 2.0398079\n", "4073 19 0.7492671 0.8883582\n", " median.asymptomatic upper_hinge.asymptomatic max.asymptomatic\n", "2181 1.3539283 1.684392 2.320280\n", "2312 1.1497797 1.493119 2.194917\n", "2443 0.9322114 1.035093 1.137974\n", "3491 1.0643892 1.279861 1.993496\n", "3853 2.3650306 2.705118 3.447242\n", "4073 0.9637596 1.151557 1.720847\n", " mean.asymptomatic anum.asymptomatic\n", "2181 1.4162796 120\n", "2312 1.2886412 120\n", "2443 0.9910522 120\n", "3491 1.1591398 120\n", "3853 2.3601770 120\n", "4073 1.0522959 120\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "'select()' returned 1:1 mapping between keys and columns\n", "\n", "Warning message in clusterProfiler::bitr(siggenes$gene, fromType = \"SYMBOL\", toType = \"ENTREZID\", :\n", "\"0.51% of input gene IDs are fail to map...\"\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[1] \"Unresolved gene symbols\"\n", "[1] \"MT-CYB\"\n", " gene p_val avg_log2FC pct.1 pct.2 p_val_adj num.symptomatic\n", "2181 ACAP1 2.404475e-07 -0.5607755 0.454 0.683 8.800620e-03 386\n", "2312 ADGRG1 9.917797e-08 0.6660579 0.615 0.425 3.630013e-03 523\n", "2443 AK6 1.002442e-10 0.3335989 0.137 0.025 3.669039e-06 117\n", "3491 AOAH 1.290840e-07 0.5226751 0.355 0.167 4.724602e-03 302\n", "3853 ARL4C 6.216770e-07 0.4597286 0.985 0.967 2.275400e-02 838\n", "4073 ATP6V1E1 1.161034e-06 0.4265228 0.321 0.158 4.249499e-02 273\n", " min.symptomatic lower_hinge.symptomatic median.symptomatic\n", "2181 0.6037215 0.9797888 1.173145\n", "2312 0.6454174 1.0973478 1.475376\n", "2443 0.6037215 0.9815590 1.084805\n", "3491 0.5217806 1.0008444 1.168544\n", "3853 0.8603849 2.3353147 2.704539\n", "4073 0.5217806 0.9694118 1.087797\n", " upper_hinge.symptomatic max.symptomatic mean.symptomatic anum.symptomatic\n", "2181 1.521782 2.731604 1.273027 851\n", "2312 1.802147 2.898007 1.514921 851\n", "2443 1.304935 2.195094 1.145239 851\n", "3491 1.438998 2.608729 1.260579 851\n", "3853 3.015161 4.134571 2.648831 851\n", "4073 1.316409 2.547769 1.164952 851\n", " num.asymptomatic min.asymptomatic lower_hinge.asymptomatic\n", "2181 82 0.7540631 1.0034633\n", "2312 51 0.7540631 0.9962486\n", "2443 3 0.9029708 0.9175911\n", "3491 20 0.9014036 0.9854102\n", "3853 116 0.9063864 2.0398079\n", "4073 19 0.7492671 0.8883582\n", " median.asymptomatic upper_hinge.asymptomatic max.asymptomatic\n", "2181 1.3539283 1.684392 2.320280\n", "2312 1.1497797 1.493119 2.194917\n", "2443 0.9322114 1.035093 1.137974\n", "3491 1.0643892 1.279861 1.993496\n", "3853 2.3650306 2.705118 3.447242\n", "4073 0.9637596 1.151557 1.720847\n", " mean.asymptomatic anum.asymptomatic\n", "2181 1.4162796 120\n", "2312 1.2886412 120\n", "2443 0.9910522 120\n", "3491 1.1591398 120\n", "3853 2.3601770 120\n", "4073 1.0522959 120\n", "[1] \"Got geneRegVec\"\n", "[1] 197\n", "[1] \"UpGenes 118\"\n", "[1] \"downGenes 79\"\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Loading required package: org.Hs.eg.db\n", "\n", "Loading required package: AnnotationDbi\n", "\n", "Loading required package: stats4\n", "\n", "Loading required package: BiocGenerics\n", "\n", "\n", "Attaching package: 'BiocGenerics'\n", "\n", "\n", "The following objects are masked from 'package:stats':\n", "\n", " IQR, mad, sd, var, xtabs\n", "\n", "\n", "The following objects are masked from 'package:base':\n", "\n", " anyDuplicated, aperm, append, as.data.frame, basename, cbind,\n", " colnames, dirname, do.call, duplicated, eval, evalq, Filter, Find,\n", " get, grep, grepl, intersect, is.unsorted, lapply, Map, mapply,\n", " match, mget, order, paste, pmax, pmax.int, pmin, pmin.int,\n", " Position, rank, rbind, Reduce, rownames, sapply, setdiff, sort,\n", " table, tapply, union, unique, unsplit, which.max, which.min\n", "\n", "\n", "Loading required package: Biobase\n", "\n", "Welcome to Bioconductor\n", "\n", " Vignettes contain introductory material; view with\n", " 'browseVignettes()'. To cite Bioconductor, see\n", " 'citation(\"Biobase\")', and for packages 'citation(\"pkgname\")'.\n", "\n", "\n", "Loading required package: IRanges\n", "\n", "Loading required package: S4Vectors\n", "\n", "\n", "Attaching package: 'S4Vectors'\n", "\n", "\n", "The following object is masked from 'package:utils':\n", "\n", " findMatches\n", "\n", "\n", "The following objects are masked from 'package:base':\n", "\n", " expand.grid, I, unname\n", "\n", "\n", "\n", "Attaching package: 'IRanges'\n", "\n", "\n", "The following object is masked from 'package:grDevices':\n", "\n", " windows\n", "\n", "\n", "Reading KEGG annotation online: \"https://rest.kegg.jp/link/hsa/pathway\"...\n", "\n", "Reading KEGG annotation online: \"https://rest.kegg.jp/list/pathway/hsa\"...\n", "\n", "Reading KEGG annotation online: \"https://rest.kegg.jp/link/hsa/module\"...\n", "\n", "Reading KEGG annotation online: \"https://rest.kegg.jp/list/module\"...\n", "\n", "--> No gene can be mapped....\n", "\n", "--> Expected input gene ID: 229,8277,5315,3417,2271,2023\n", "\n", "--> return NULL...\n", "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[1] \"1\"\n", "[1] \"Selecting Genes\"\n", " gene p_val avg_log2FC pct.1 pct.2 p_val_adj num.symptomatic\n", "2188 AKNA 8.446774e-13 0.7462365 0.790 0.552 3.091604e-08 271\n", "2994 ALOX5AP 1.520502e-07 0.5119407 0.752 0.538 5.565191e-03 258\n", "3108 ANXA1 6.230337e-08 0.4934585 0.787 0.601 2.280366e-03 270\n", "3620 ATP5F1E 1.243636e-06 0.3695070 0.965 0.964 4.551833e-02 331\n", "3651 ATP6V1E1 7.744100e-07 0.4605031 0.379 0.193 2.834418e-02 130\n", "3808 BCL2L11 3.344831e-07 0.4061517 0.178 0.045 1.224242e-02 61\n", " min.symptomatic lower_hinge.symptomatic median.symptomatic\n", "2188 0.6736820 1.1683908 1.652384\n", "2994 0.7395776 1.3086686 1.664429\n", "3108 0.7650776 1.3546013 1.795799\n", "3620 0.9660725 2.2290611 2.472113\n", "3651 0.7199874 0.9522740 1.095699\n", "3808 0.5682135 0.9463514 1.053167\n", " upper_hinge.symptomatic max.symptomatic mean.symptomatic anum.symptomatic\n", "2188 1.966748 3.441761 1.651798 343\n", "2994 2.090693 3.704510 1.695536 343\n", "3108 2.149073 3.056667 1.770160 343\n", "3620 2.739857 3.530738 2.435742 343\n", "3651 1.379211 2.653949 1.210094 343\n", "3808 1.390887 2.218604 1.200571 343\n", " num.asymptomatic min.asymptomatic lower_hinge.asymptomatic\n", "2188 123 0.6453312 1.1541607\n", "2994 120 0.6036464 1.2094256\n", "3108 134 0.9151822 1.1816925\n", "3620 215 0.8521268 1.8337718\n", "3651 43 0.7745385 0.9932898\n", "3808 10 0.6453312 0.8749674\n", " median.asymptomatic upper_hinge.asymptomatic max.asymptomatic\n", "2188 1.346866 1.727664 2.622593\n", "2994 1.598664 1.984988 3.041986\n", "3108 1.531440 2.001572 2.901368\n", "3620 2.226555 2.474896 3.287677\n", "3651 1.129118 1.276915 1.822985\n", "3808 1.195663 1.277593 1.810189\n", " mean.asymptomatic anum.asymptomatic\n", "2188 1.450461 223\n", "2994 1.625875 223\n", "3108 1.627668 223\n", "3620 2.163553 223\n", "3651 1.171662 223\n", "3808 1.162602 223\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "'select()' returned 1:1 mapping between keys and columns\n", "\n", "Warning message in clusterProfiler::bitr(siggenes$gene, fromType = \"SYMBOL\", toType = \"ENTREZID\", :\n", "\"1.68% of input gene IDs are fail to map...\"\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[1] \"Unresolved gene symbols\"\n", "[1] \"GRASP\" \"WARS\" \n", " gene p_val avg_log2FC pct.1 pct.2 p_val_adj num.symptomatic\n", "2188 AKNA 8.446774e-13 0.7462365 0.790 0.552 3.091604e-08 271\n", "2994 ALOX5AP 1.520502e-07 0.5119407 0.752 0.538 5.565191e-03 258\n", "3108 ANXA1 6.230337e-08 0.4934585 0.787 0.601 2.280366e-03 270\n", "3620 ATP5F1E 1.243636e-06 0.3695070 0.965 0.964 4.551833e-02 331\n", "3651 ATP6V1E1 7.744100e-07 0.4605031 0.379 0.193 2.834418e-02 130\n", "3808 BCL2L11 3.344831e-07 0.4061517 0.178 0.045 1.224242e-02 61\n", " min.symptomatic lower_hinge.symptomatic median.symptomatic\n", "2188 0.6736820 1.1683908 1.652384\n", "2994 0.7395776 1.3086686 1.664429\n", "3108 0.7650776 1.3546013 1.795799\n", "3620 0.9660725 2.2290611 2.472113\n", "3651 0.7199874 0.9522740 1.095699\n", "3808 0.5682135 0.9463514 1.053167\n", " upper_hinge.symptomatic max.symptomatic mean.symptomatic anum.symptomatic\n", "2188 1.966748 3.441761 1.651798 343\n", "2994 2.090693 3.704510 1.695536 343\n", "3108 2.149073 3.056667 1.770160 343\n", "3620 2.739857 3.530738 2.435742 343\n", "3651 1.379211 2.653949 1.210094 343\n", "3808 1.390887 2.218604 1.200571 343\n", " num.asymptomatic min.asymptomatic lower_hinge.asymptomatic\n", "2188 123 0.6453312 1.1541607\n", "2994 120 0.6036464 1.2094256\n", "3108 134 0.9151822 1.1816925\n", "3620 215 0.8521268 1.8337718\n", "3651 43 0.7745385 0.9932898\n", "3808 10 0.6453312 0.8749674\n", " median.asymptomatic upper_hinge.asymptomatic max.asymptomatic\n", "2188 1.346866 1.727664 2.622593\n", "2994 1.598664 1.984988 3.041986\n", "3108 1.531440 2.001572 2.901368\n", "3620 2.226555 2.474896 3.287677\n", "3651 1.129118 1.276915 1.822985\n", "3808 1.195663 1.277593 1.810189\n", " mean.asymptomatic anum.asymptomatic\n", "2188 1.450461 223\n", "2994 1.625875 223\n", "3108 1.627668 223\n", "3620 2.163553 223\n", "3651 1.171662 223\n", "3808 1.162602 223\n", "[1] \"Got geneRegVec\"\n", "[1] 117\n", "[1] \"UpGenes 71\"\n", "[1] \"downGenes 46\"\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Reading KEGG annotation online: \"https://rest.kegg.jp/link/hsa/pathway\"...\n", "\n", "Reading KEGG annotation online: \"https://rest.kegg.jp/list/pathway/hsa\"...\n", "\n", "Reading KEGG annotation online: \"https://rest.kegg.jp/link/hsa/module\"...\n", "\n", "Reading KEGG annotation online: \"https://rest.kegg.jp/list/module\"...\n", "\n", "--> No gene can be mapped....\n", "\n", "--> Expected input gene ID: 2203,229,6389,2026,4190,5106\n", "\n", "--> return NULL...\n", "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[1] \"2\"\n", "[1] \"Selecting Genes\"\n", " gene p_val avg_log2FC pct.1 pct.2 p_val_adj num.symptomatic\n", "1123 ACTB 1.797763e-07 0.3600201 1.000 1.00 6.579993e-03 89\n", "3158 CDC42EP3 1.265918e-06 -0.7981103 0.652 0.90 4.633387e-02 58\n", "3495 CLU 8.941124e-07 0.9149190 0.775 0.35 3.272541e-02 69\n", "5887 HLA-DQB1 1.013789e-07 -1.3767247 0.528 0.85 3.710569e-03 47\n", "6102 IFI44L 1.704925e-11 -1.6106701 0.393 0.85 6.240196e-07 35\n", "6103 IFI6 2.654996e-07 -0.9445046 0.663 0.95 9.717551e-03 59\n", " min.symptomatic lower_hinge.symptomatic median.symptomatic\n", "1123 4.0889920 4.4882855 4.6466920\n", "3158 0.3029206 0.7839249 1.0674145\n", "3495 0.3315628 0.9782325 1.2883342\n", "5887 0.4225485 0.6294704 0.8387054\n", "6102 0.4404877 0.7811740 1.1019120\n", "6103 0.4113333 1.0267922 1.7594221\n", " upper_hinge.symptomatic max.symptomatic mean.symptomatic anum.symptomatic\n", "1123 4.878424 5.353059 4.6643553 89\n", "3158 1.324052 3.021948 1.0711944 89\n", "3495 1.707905 2.470082 1.3009511 89\n", "5887 1.362533 2.155525 0.9807504 89\n", "6102 1.615501 2.142220 1.1860052 89\n", "6103 2.599172 3.460054 1.8279910 89\n", " num.asymptomatic min.asymptomatic lower_hinge.asymptomatic\n", "1123 40 4.0596571 4.3009724\n", "3158 36 0.6234093 1.0850716\n", "3495 14 0.5148308 0.7074999\n", "5887 34 0.5266728 1.2318537\n", "6102 34 1.1480872 1.5993389\n", "6103 38 0.5208910 1.8656302\n", " median.asymptomatic upper_hinge.asymptomatic max.asymptomatic\n", "1123 4.4142608 4.565288 4.914171\n", "3158 1.4447776 1.834904 2.277842\n", "3495 0.8956615 1.365963 2.058635\n", "5887 1.5901625 1.903104 3.060188\n", "6102 1.8862175 2.138386 2.708162\n", "6103 2.5183022 2.881605 3.403000\n", " mean.asymptomatic anum.asymptomatic\n", "1123 4.434751 40\n", "3158 1.463091 40\n", "3495 1.070859 40\n", "5887 1.619287 40\n", "6102 1.894887 40\n", "6103 2.353985 40\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "'select()' returned 1:1 mapping between keys and columns\n", "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[1] \"Unresolved gene symbols\"\n", "character(0)\n", " gene p_val avg_log2FC pct.1 pct.2 p_val_adj num.symptomatic\n", "1123 ACTB 1.797763e-07 0.3600201 1.000 1.00 6.579993e-03 89\n", "3158 CDC42EP3 1.265918e-06 -0.7981103 0.652 0.90 4.633387e-02 58\n", "3495 CLU 8.941124e-07 0.9149190 0.775 0.35 3.272541e-02 69\n", "5887 HLA-DQB1 1.013789e-07 -1.3767247 0.528 0.85 3.710569e-03 47\n", "6102 IFI44L 1.704925e-11 -1.6106701 0.393 0.85 6.240196e-07 35\n", "6103 IFI6 2.654996e-07 -0.9445046 0.663 0.95 9.717551e-03 59\n", " min.symptomatic lower_hinge.symptomatic median.symptomatic\n", "1123 4.0889920 4.4882855 4.6466920\n", "3158 0.3029206 0.7839249 1.0674145\n", "3495 0.3315628 0.9782325 1.2883342\n", "5887 0.4225485 0.6294704 0.8387054\n", "6102 0.4404877 0.7811740 1.1019120\n", "6103 0.4113333 1.0267922 1.7594221\n", " upper_hinge.symptomatic max.symptomatic mean.symptomatic anum.symptomatic\n", "1123 4.878424 5.353059 4.6643553 89\n", "3158 1.324052 3.021948 1.0711944 89\n", "3495 1.707905 2.470082 1.3009511 89\n", "5887 1.362533 2.155525 0.9807504 89\n", "6102 1.615501 2.142220 1.1860052 89\n", "6103 2.599172 3.460054 1.8279910 89\n", " num.asymptomatic min.asymptomatic lower_hinge.asymptomatic\n", "1123 40 4.0596571 4.3009724\n", "3158 36 0.6234093 1.0850716\n", "3495 14 0.5148308 0.7074999\n", "5887 34 0.5266728 1.2318537\n", "6102 34 1.1480872 1.5993389\n", "6103 38 0.5208910 1.8656302\n", " median.asymptomatic upper_hinge.asymptomatic max.asymptomatic\n", "1123 4.4142608 4.565288 4.914171\n", "3158 1.4447776 1.834904 2.277842\n", "3495 0.8956615 1.365963 2.058635\n", "5887 1.5901625 1.903104 3.060188\n", "6102 1.8862175 2.138386 2.708162\n", "6103 2.5183022 2.881605 3.403000\n", " mean.asymptomatic anum.asymptomatic\n", "1123 4.434751 40\n", "3158 1.463091 40\n", "3495 1.070859 40\n", "5887 1.619287 40\n", "6102 1.894887 40\n", "6103 2.353985 40\n", "[1] \"Got geneRegVec\"\n", "[1] 16\n", "[1] \"UpGenes 5\"\n", "[1] \"downGenes 11\"\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Reading KEGG annotation online: \"https://rest.kegg.jp/link/hsa/pathway\"...\n", "\n", "Reading KEGG annotation online: \"https://rest.kegg.jp/list/pathway/hsa\"...\n", "\n", "Reading KEGG annotation online: \"https://rest.kegg.jp/link/hsa/module\"...\n", "\n", "Reading KEGG annotation online: \"https://rest.kegg.jp/list/module\"...\n", "\n", "--> No gene can be mapped....\n", "\n", "--> Expected input gene ID: 3417,25796,1431,2027,9563,7086\n", "\n", "--> return NULL...\n", "\n", "--> No gene can be mapped....\n", "\n", "--> Expected input gene ID: 5230,3417,5634,8789,221823,2539\n", "\n", "--> return NULL...\n", "\n", "--> No gene can be mapped....\n", "\n", "--> Expected input gene ID: 6389,3421,2203,5226,5211,3101\n", "\n", "--> return NULL...\n", "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[1] \"3\"\n", "[1] \"Selecting Genes\"\n", " gene p_val avg_log2FC pct.1 pct.2 p_val_adj num.symptomatic\n", "27 ABCA6 8.233984e-07 0.4610089 0.166 0.031 3.013720e-02 51\n", "3570 B2M 1.220255e-07 -0.3571679 0.997 1.000 4.466254e-03 306\n", "3713 BHLHE40 5.889103e-09 0.6744956 0.244 0.079 2.155470e-04 75\n", "3728 BIRC3 1.845030e-09 1.3200474 0.625 0.465 6.752993e-05 192\n", "5135 CREM 5.949722e-09 1.2795141 0.280 0.134 2.177658e-04 86\n", "5960 EIF2AK2 9.370808e-08 -0.6118647 0.384 0.654 3.429810e-03 118\n", " min.symptomatic lower_hinge.symptomatic median.symptomatic\n", "27 0.4154238 0.8381902 1.248612\n", "3570 1.9767033 4.0798996 4.370182\n", "3713 0.4154238 0.8337894 1.035037\n", "3728 0.4801834 1.2323859 1.694900\n", "5135 0.5617887 0.9691072 1.721952\n", "5960 0.3545069 0.8338882 1.120258\n", " upper_hinge.symptomatic max.symptomatic mean.symptomatic anum.symptomatic\n", "27 1.580820 2.541549 1.250322 307\n", "3570 4.620237 5.328464 4.287898 307\n", "3713 1.610178 2.650447 1.243757 307\n", "3728 2.307884 4.314828 1.803687 307\n", "5135 2.425489 3.556398 1.761281 307\n", "5960 1.493721 3.230468 1.220742 307\n", " num.asymptomatic min.asymptomatic lower_hinge.asymptomatic\n", "27 4 0.8443374 0.8882744\n", "3570 127 2.7948994 4.4474565\n", "3713 10 0.5785861 0.6744841\n", "3728 59 0.6182753 0.8644852\n", "5135 17 0.5453216 0.8229916\n", "5960 83 0.5924827 0.9953851\n", " median.asymptomatic upper_hinge.asymptomatic max.asymptomatic\n", "27 1.1391704 1.446560 1.546991\n", "3570 4.6316776 4.802902 5.480680\n", "3713 0.8513474 1.003792 1.143916\n", "3728 1.2312236 1.573659 2.261602\n", "5135 0.8723655 1.237441 1.592535\n", "5960 1.3258771 1.726349 2.926802\n", " mean.asymptomatic anum.asymptomatic\n", "27 1.1674172 127\n", "3570 4.5569879 127\n", "3713 0.8389125 127\n", "3728 1.2663952 127\n", "5135 0.9928600 127\n", "5960 1.3814101 127\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "'select()' returned 1:1 mapping between keys and columns\n", "\n", "Warning message in clusterProfiler::bitr(siggenes$gene, fromType = \"SYMBOL\", toType = \"ENTREZID\", :\n", "\"4.17% of input gene IDs are fail to map...\"\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[1] \"Unresolved gene symbols\"\n", "[1] \"GRASP\" \"WARS\" \n", " gene p_val avg_log2FC pct.1 pct.2 p_val_adj num.symptomatic\n", "27 ABCA6 8.233984e-07 0.4610089 0.166 0.031 3.013720e-02 51\n", "3570 B2M 1.220255e-07 -0.3571679 0.997 1.000 4.466254e-03 306\n", "3713 BHLHE40 5.889103e-09 0.6744956 0.244 0.079 2.155470e-04 75\n", "3728 BIRC3 1.845030e-09 1.3200474 0.625 0.465 6.752993e-05 192\n", "5135 CREM 5.949722e-09 1.2795141 0.280 0.134 2.177658e-04 86\n", "5960 EIF2AK2 9.370808e-08 -0.6118647 0.384 0.654 3.429810e-03 118\n", " min.symptomatic lower_hinge.symptomatic median.symptomatic\n", "27 0.4154238 0.8381902 1.248612\n", "3570 1.9767033 4.0798996 4.370182\n", "3713 0.4154238 0.8337894 1.035037\n", "3728 0.4801834 1.2323859 1.694900\n", "5135 0.5617887 0.9691072 1.721952\n", "5960 0.3545069 0.8338882 1.120258\n", " upper_hinge.symptomatic max.symptomatic mean.symptomatic anum.symptomatic\n", "27 1.580820 2.541549 1.250322 307\n", "3570 4.620237 5.328464 4.287898 307\n", "3713 1.610178 2.650447 1.243757 307\n", "3728 2.307884 4.314828 1.803687 307\n", "5135 2.425489 3.556398 1.761281 307\n", "5960 1.493721 3.230468 1.220742 307\n", " num.asymptomatic min.asymptomatic lower_hinge.asymptomatic\n", "27 4 0.8443374 0.8882744\n", "3570 127 2.7948994 4.4474565\n", "3713 10 0.5785861 0.6744841\n", "3728 59 0.6182753 0.8644852\n", "5135 17 0.5453216 0.8229916\n", "5960 83 0.5924827 0.9953851\n", " median.asymptomatic upper_hinge.asymptomatic max.asymptomatic\n", "27 1.1391704 1.446560 1.546991\n", "3570 4.6316776 4.802902 5.480680\n", "3713 0.8513474 1.003792 1.143916\n", "3728 1.2312236 1.573659 2.261602\n", "5135 0.8723655 1.237441 1.592535\n", "5960 1.3258771 1.726349 2.926802\n", " mean.asymptomatic anum.asymptomatic\n", "27 1.1674172 127\n", "3570 4.5569879 127\n", "3713 0.8389125 127\n", "3728 1.2663952 127\n", "5135 0.9928600 127\n", "5960 1.3814101 127\n", "[1] \"Got geneRegVec\"\n", "[1] 46\n", "[1] \"UpGenes 27\"\n", "[1] \"downGenes 19\"\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Reading KEGG annotation online: \"https://rest.kegg.jp/link/hsa/pathway\"...\n", "\n", "Reading KEGG annotation online: \"https://rest.kegg.jp/list/pathway/hsa\"...\n", "\n", "Reading KEGG annotation online: \"https://rest.kegg.jp/link/hsa/module\"...\n", "\n", "Reading KEGG annotation online: \"https://rest.kegg.jp/list/module\"...\n", "\n", "--> No gene can be mapped....\n", "\n", "--> Expected input gene ID: 5634,8803,5313,2597,8801,2203\n", "\n", "--> return NULL...\n", "\n", "--> No gene can be mapped....\n", "\n", "--> Expected input gene ID: 387712,221823,3417,55753,226,84076\n", "\n", "--> return NULL...\n", "\n", "--> No gene can be mapped....\n", "\n", "--> Expected input gene ID: 387712,55753,5106,2026,8277,25796\n", "\n", "--> return NULL...\n", "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[1] \"4\"\n", "[1] \"Selecting Genes\"\n", " gene p_val avg_log2FC pct.1 pct.2 p_val_adj num.symptomatic\n", "2956 BIRC3 1.875762e-09 1.2729489 0.893 0.623 6.865477e-05 159\n", "3656 CDC42SE1 4.247712e-07 0.8427693 0.506 0.213 1.554705e-02 90\n", "5617 FOS 6.161184e-08 -1.2255440 0.584 0.852 2.255055e-03 104\n", "6061 GRASP 1.857235e-08 1.2881419 0.669 0.361 6.797667e-04 119\n", "6377 HLA-DQB1 4.832364e-09 -0.8589260 0.933 0.984 1.768694e-04 166\n", "6411 HMGXB4 5.018071e-08 0.7278942 0.270 0.049 1.836664e-03 48\n", " min.symptomatic lower_hinge.symptomatic median.symptomatic\n", "2956 0.6322469 1.5775573 2.094639\n", "3656 0.5938594 1.0706167 1.339393\n", "5617 0.6322469 1.1820717 1.694444\n", "6061 0.5938594 1.2280137 1.764777\n", "6377 0.8662513 2.0517491 2.389258\n", "6411 0.6322469 0.9638798 1.195536\n", " upper_hinge.symptomatic max.symptomatic mean.symptomatic anum.symptomatic\n", "2956 2.679498 3.840798 2.132117 178\n", "3656 1.578033 2.603848 1.364602 178\n", "5617 2.173132 4.175690 1.738836 178\n", "6061 2.299576 3.618930 1.777873 178\n", "6377 2.674975 3.756475 2.341663 178\n", "6411 1.631729 2.354934 1.286075 178\n", " num.asymptomatic min.asymptomatic lower_hinge.asymptomatic\n", "2956 38 1.0352715 1.1838904\n", "3656 13 0.8293475 1.0585455\n", "5617 52 1.1084414 1.5459949\n", "6061 22 0.9466408 1.1206900\n", "6377 60 1.2940705 2.6544566\n", "6411 3 0.8293475 0.9325438\n", " median.asymptomatic upper_hinge.asymptomatic max.asymptomatic\n", "2956 1.506651 1.972074 2.966108\n", "3656 1.192934 1.240751 1.635873\n", "5617 2.192529 2.963098 3.810507\n", "6061 1.255677 1.532933 2.323597\n", "6377 3.002796 3.220972 3.818978\n", "6411 1.035740 1.040535 1.045330\n", " mean.asymptomatic anum.asymptomatic\n", "2956 1.6381812 61\n", "3656 1.1647822 61\n", "5617 2.2838235 61\n", "6061 1.3721042 61\n", "6377 2.8858005 61\n", "6411 0.9701393 61\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "'select()' returned 1:1 mapping between keys and columns\n", "\n", "Warning message in clusterProfiler::bitr(siggenes$gene, fromType = \"SYMBOL\", toType = \"ENTREZID\", :\n", "\"2.94% of input gene IDs are fail to map...\"\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[1] \"Unresolved gene symbols\"\n", "[1] \"GRASP\"\n", " gene p_val avg_log2FC pct.1 pct.2 p_val_adj num.symptomatic\n", "2956 BIRC3 1.875762e-09 1.2729489 0.893 0.623 6.865477e-05 159\n", "3656 CDC42SE1 4.247712e-07 0.8427693 0.506 0.213 1.554705e-02 90\n", "5617 FOS 6.161184e-08 -1.2255440 0.584 0.852 2.255055e-03 104\n", "6377 HLA-DQB1 4.832364e-09 -0.8589260 0.933 0.984 1.768694e-04 166\n", "6411 HMGXB4 5.018071e-08 0.7278942 0.270 0.049 1.836664e-03 48\n", "6883 ITGB2-AS1 5.061181e-07 0.4090119 0.140 0.000 1.852443e-02 25\n", " min.symptomatic lower_hinge.symptomatic median.symptomatic\n", "2956 0.6322469 1.5775573 2.094639\n", "3656 0.5938594 1.0706167 1.339393\n", "5617 0.6322469 1.1820717 1.694444\n", "6377 0.8662513 2.0517491 2.389258\n", "6411 0.6322469 0.9638798 1.195536\n", "6883 0.7357068 1.0074197 1.184363\n", " upper_hinge.symptomatic max.symptomatic mean.symptomatic anum.symptomatic\n", "2956 2.679498 3.840798 2.132117 178\n", "3656 1.578033 2.603848 1.364602 178\n", "5617 2.173132 4.175690 1.738836 178\n", "6377 2.674975 3.756475 2.341663 178\n", "6411 1.631729 2.354934 1.286075 178\n", "6883 1.334677 1.685346 1.167166 178\n", " num.asymptomatic min.asymptomatic lower_hinge.asymptomatic\n", "2956 38 1.0352715 1.1838904\n", "3656 13 0.8293475 1.0585455\n", "5617 52 1.1084414 1.5459949\n", "6377 60 1.2940705 2.6544566\n", "6411 3 0.8293475 0.9325438\n", "6883 NA NA NA\n", " median.asymptomatic upper_hinge.asymptomatic max.asymptomatic\n", "2956 1.506651 1.972074 2.966108\n", "3656 1.192934 1.240751 1.635873\n", "5617 2.192529 2.963098 3.810507\n", "6377 3.002796 3.220972 3.818978\n", "6411 1.035740 1.040535 1.045330\n", "6883 NA NA NA\n", " mean.asymptomatic anum.asymptomatic\n", "2956 1.6381812 61\n", "3656 1.1647822 61\n", "5617 2.2838235 61\n", "6377 2.8858005 61\n", "6411 0.9701393 61\n", "6883 NA NA\n", "[1] \"Got geneRegVec\"\n", "[1] 33\n", "[1] \"UpGenes 16\"\n", "[1] \"downGenes 17\"\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Reading KEGG annotation online: \"https://rest.kegg.jp/link/hsa/pathway\"...\n", "\n", "Reading KEGG annotation online: \"https://rest.kegg.jp/list/pathway/hsa\"...\n", "\n", "Reading KEGG annotation online: \"https://rest.kegg.jp/link/hsa/module\"...\n", "\n", "Reading KEGG annotation online: \"https://rest.kegg.jp/list/module\"...\n", "\n", "No gene sets have size between 10 and 500 ...\n", "\n", "--> return NULL...\n", "\n", "No gene sets have size between 10 and 500 ...\n", "\n", "--> return NULL...\n", "\n", "--> No gene can be mapped....\n", "\n", "--> Expected input gene ID: 6120,5634,3101,2203,7167,2026\n", "\n", "--> return NULL...\n", "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[1] \"5\"\n", "[1] \"Selecting Genes\"\n", " gene p_val avg_log2FC pct.1 pct.2 p_val_adj\n", "20 ABCA1 3.890295e-11 1.519858 0.169 0.000 1.423887e-06\n", "347 AC016831.5 2.655144e-07 1.071951 0.108 0.000 9.718091e-03\n", "985 AGO2 6.872647e-13 1.811178 0.197 0.000 2.515457e-08\n", "1749 ASAH1 2.044995e-07 1.849526 0.317 0.067 7.484885e-03\n", "1839 ATP2A2 3.310180e-07 1.027950 0.112 0.000 1.211559e-02\n", "1977 BASP1 2.642423e-13 2.013685 0.201 0.000 9.671533e-09\n", " num.symptomatic min.symptomatic lower_hinge.symptomatic median.symptomatic\n", "20 42 0.8316845 2.133039 2.476056\n", "347 27 1.0168967 2.001843 2.464082\n", "985 49 0.1776767 2.323597 2.633319\n", "1749 79 0.1776767 2.290474 2.523157\n", "1839 28 0.1776767 1.492053 2.442911\n", "1977 50 1.0868847 2.436025 2.770155\n", " upper_hinge.symptomatic max.symptomatic mean.symptomatic anum.symptomatic\n", "20 2.728255 3.413408 2.386340 249\n", "347 2.716962 2.999922 2.268918 249\n", "985 2.787718 3.629587 2.504058 249\n", "1749 2.824435 3.800684 2.475336 249\n", "1839 2.685172 3.010868 2.106698 249\n", "1977 2.944534 3.475033 2.673837 249\n", " num.asymptomatic min.asymptomatic lower_hinge.asymptomatic\n", "20 NA NA NA\n", "347 NA NA NA\n", "985 NA NA NA\n", "1749 2 1.850633 1.850633\n", "1839 NA NA NA\n", "1977 NA NA NA\n", " median.asymptomatic upper_hinge.asymptomatic max.asymptomatic\n", "20 NA NA NA\n", "347 NA NA NA\n", "985 NA NA NA\n", "1749 1.900507 1.950381 1.950381\n", "1839 NA NA NA\n", "1977 NA NA NA\n", " mean.asymptomatic anum.asymptomatic\n", "20 NA NA\n", "347 NA NA\n", "985 NA NA\n", "1749 1.900507 30\n", "1839 NA NA\n", "1977 NA NA\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "'select()' returned 1:1 mapping between keys and columns\n", "\n", "Warning message in clusterProfiler::bitr(siggenes$gene, fromType = \"SYMBOL\", toType = \"ENTREZID\", :\n", "\"2.15% of input gene IDs are fail to map...\"\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[1] \"Unresolved gene symbols\"\n", "[1] \"AC016831.5\" \"MARCH6\" \n", " gene p_val avg_log2FC pct.1 pct.2 p_val_adj num.symptomatic\n", "20 ABCA1 3.890295e-11 1.519858 0.169 0.000 1.423887e-06 42\n", "985 AGO2 6.872647e-13 1.811178 0.197 0.000 2.515457e-08 49\n", "1749 ASAH1 2.044995e-07 1.849526 0.317 0.067 7.484885e-03 79\n", "1839 ATP2A2 3.310180e-07 1.027950 0.112 0.000 1.211559e-02 28\n", "1977 BASP1 2.642423e-13 2.013685 0.201 0.000 9.671533e-09 50\n", "2061 BIRC6 6.841965e-09 1.513423 0.137 0.000 2.504228e-04 34\n", " min.symptomatic lower_hinge.symptomatic median.symptomatic\n", "20 0.8316845 2.133039 2.476056\n", "985 0.1776767 2.323597 2.633319\n", "1749 0.1776767 2.290474 2.523157\n", "1839 0.1776767 1.492053 2.442911\n", "1977 1.0868847 2.436025 2.770155\n", "2061 1.0500172 1.971365 2.542914\n", " upper_hinge.symptomatic max.symptomatic mean.symptomatic anum.symptomatic\n", "20 2.728255 3.413408 2.386340 249\n", "985 2.787718 3.629587 2.504058 249\n", "1749 2.824435 3.800684 2.475336 249\n", "1839 2.685172 3.010868 2.106698 249\n", "1977 2.944534 3.475033 2.673837 249\n", "2061 2.895581 3.771215 2.459941 249\n", " num.asymptomatic min.asymptomatic lower_hinge.asymptomatic\n", "20 NA NA NA\n", "985 NA NA NA\n", "1749 2 1.850633 1.850633\n", "1839 NA NA NA\n", "1977 NA NA NA\n", "2061 NA NA NA\n", " median.asymptomatic upper_hinge.asymptomatic max.asymptomatic\n", "20 NA NA NA\n", "985 NA NA NA\n", "1749 1.900507 1.950381 1.950381\n", "1839 NA NA NA\n", "1977 NA NA NA\n", "2061 NA NA NA\n", " mean.asymptomatic anum.asymptomatic\n", "20 NA NA\n", "985 NA NA\n", "1749 1.900507 30\n", "1839 NA NA\n", "1977 NA NA\n", "2061 NA NA\n", "[1] \"Got geneRegVec\"\n", "[1] 91\n", "[1] \"UpGenes 71\"\n", "[1] \"downGenes 20\"\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Reading KEGG annotation online: \"https://rest.kegg.jp/link/hsa/pathway\"...\n", "\n", "Reading KEGG annotation online: \"https://rest.kegg.jp/list/pathway/hsa\"...\n", "\n", "Reading KEGG annotation online: \"https://rest.kegg.jp/link/hsa/module\"...\n", "\n", "Reading KEGG annotation online: \"https://rest.kegg.jp/list/module\"...\n", "\n", "--> No gene can be mapped....\n", "\n", "--> Expected input gene ID: 5106,22934,6390,50,3418,3099\n", "\n", "--> return NULL...\n", "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[1] \"6\"\n", "[1] \"Selecting Genes\"\n", " gene p_val avg_log2FC pct.1 pct.2 p_val_adj num.symptomatic\n", "1105 ACBD3 1.118523e-10 1.0477991 0.104 0 4.093905e-06 41\n", "1286 AGPAT4 7.581936e-11 0.9909809 0.107 0 2.775064e-06 42\n", "1902 ANP32E 7.849500e-12 1.1886457 0.117 0 2.872996e-07 46\n", "2192 ARPC4 1.458102e-13 1.4146797 0.135 0 5.336798e-09 53\n", "2201 ARRDC3 4.027519e-11 1.0292950 0.109 0 1.474112e-06 43\n", "2429 BACH1 2.231486e-10 1.0727394 0.102 0 8.167461e-06 40\n", " min.symptomatic lower_hinge.symptomatic median.symptomatic\n", "1105 1.659585 1.916496 2.324845\n", "1286 1.556568 1.851236 2.097143\n", "1902 1.460213 1.962901 2.441596\n", "2192 1.324052 2.207243 2.498211\n", "2201 1.324052 1.892283 2.146267\n", "2429 1.491715 1.977105 2.378269\n", " upper_hinge.symptomatic max.symptomatic mean.symptomatic anum.symptomatic\n", "1105 2.740372 3.162282 2.327573 394\n", "1286 2.571994 3.488782 2.214888 394\n", "1902 2.786288 3.451077 2.375832 394\n", "2192 2.839257 3.642174 2.484293 394\n", "2201 2.633944 3.018240 2.258153 394\n", "2429 2.815335 3.040721 2.365042 394\n", " num.asymptomatic min.asymptomatic lower_hinge.asymptomatic\n", "1105 NA NA NA\n", "1286 NA NA NA\n", "1902 NA NA NA\n", "2192 NA NA NA\n", "2201 NA NA NA\n", "2429 NA NA NA\n", " median.asymptomatic upper_hinge.asymptomatic max.asymptomatic\n", "1105 NA NA NA\n", "1286 NA NA NA\n", "1902 NA NA NA\n", "2192 NA NA NA\n", "2201 NA NA NA\n", "2429 NA NA NA\n", " mean.asymptomatic anum.asymptomatic\n", "1105 NA NA\n", "1286 NA NA\n", "1902 NA NA\n", "2192 NA NA\n", "2201 NA NA\n", "2429 NA NA\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "'select()' returned 1:1 mapping between keys and columns\n", "\n", "Warning message in clusterProfiler::bitr(siggenes$gene, fromType = \"SYMBOL\", toType = \"ENTREZID\", :\n", "\"1.64% of input gene IDs are fail to map...\"\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[1] \"Unresolved gene symbols\"\n", "[1] \"ELMSAN1\"\n", " gene p_val avg_log2FC pct.1 pct.2 p_val_adj num.symptomatic\n", "1105 ACBD3 1.118523e-10 1.0477991 0.104 0 4.093905e-06 41\n", "1286 AGPAT4 7.581936e-11 0.9909809 0.107 0 2.775064e-06 42\n", "1902 ANP32E 7.849500e-12 1.1886457 0.117 0 2.872996e-07 46\n", "2192 ARPC4 1.458102e-13 1.4146797 0.135 0 5.336798e-09 53\n", "2201 ARRDC3 4.027519e-11 1.0292950 0.109 0 1.474112e-06 43\n", "2429 BACH1 2.231486e-10 1.0727394 0.102 0 8.167461e-06 40\n", " min.symptomatic lower_hinge.symptomatic median.symptomatic\n", "1105 1.659585 1.916496 2.324845\n", "1286 1.556568 1.851236 2.097143\n", "1902 1.460213 1.962901 2.441596\n", "2192 1.324052 2.207243 2.498211\n", "2201 1.324052 1.892283 2.146267\n", "2429 1.491715 1.977105 2.378269\n", " upper_hinge.symptomatic max.symptomatic mean.symptomatic anum.symptomatic\n", "1105 2.740372 3.162282 2.327573 394\n", "1286 2.571994 3.488782 2.214888 394\n", "1902 2.786288 3.451077 2.375832 394\n", "2192 2.839257 3.642174 2.484293 394\n", "2201 2.633944 3.018240 2.258153 394\n", "2429 2.815335 3.040721 2.365042 394\n", " num.asymptomatic min.asymptomatic lower_hinge.asymptomatic\n", "1105 NA NA NA\n", "1286 NA NA NA\n", "1902 NA NA NA\n", "2192 NA NA NA\n", "2201 NA NA NA\n", "2429 NA NA NA\n", " median.asymptomatic upper_hinge.asymptomatic max.asymptomatic\n", "1105 NA NA NA\n", "1286 NA NA NA\n", "1902 NA NA NA\n", "2192 NA NA NA\n", "2201 NA NA NA\n", "2429 NA NA NA\n", " mean.asymptomatic anum.asymptomatic\n", "1105 NA NA\n", "1286 NA NA\n", "1902 NA NA\n", "2192 NA NA\n", "2201 NA NA\n", "2429 NA NA\n", "[1] \"Got geneRegVec\"\n", "[1] 60\n", "[1] \"UpGenes 60\"\n", "[1] \"downGenes 0\"\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "--> No gene can be mapped....\n", "\n", "--> Expected input gene ID: 95,5270,4688,5296,7098,5869\n", "\n", "--> return NULL...\n", "\n", "Reading KEGG annotation online: \"https://rest.kegg.jp/link/hsa/pathway\"...\n", "\n", "Reading KEGG annotation online: \"https://rest.kegg.jp/list/pathway/hsa\"...\n", "\n", "--> No gene can be mapped....\n", "\n", "--> Expected input gene ID: 31,5223,5224,10455,7363,10941\n", "\n", "--> return NULL...\n", "\n", "--> No gene can be mapped....\n", "\n", "--> Expected input gene ID: 8813,29954,9488,440138,349152,79087\n", "\n", "--> return NULL...\n", "\n", "Reading KEGG annotation online: \"https://rest.kegg.jp/link/hsa/module\"...\n", "\n", "Reading KEGG annotation online: \"https://rest.kegg.jp/list/module\"...\n", "\n", "--> No gene can be mapped....\n", "\n", "--> Expected input gene ID: 3417,4191,5631,84076,6888,6389\n", "\n", "--> return NULL...\n", "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[1] \"7\"\n", "[1] \"Selecting Genes\"\n", " gene p_val avg_log2FC pct.1 pct.2 p_val_adj num.symptomatic\n", "6378 IFI44L 4.170671e-07 -0.9870061 0.212 0.558 1.526507e-02 22\n", "6379 IFI6 8.671057e-07 -1.0030088 0.269 0.519 3.173693e-02 28\n", "6610 ISG15 4.491263e-07 -1.4168055 0.375 0.581 1.643847e-02 39\n", "7491 LY6E 5.056386e-11 -1.1795152 0.442 0.752 1.850688e-06 46\n", "10637 RPL23 5.288338e-07 -0.5722454 0.885 0.977 1.935585e-02 92\n", "10660 RPL39L 3.914921e-21 -1.4306520 0.029 0.558 1.432900e-16 3\n", " min.symptomatic lower_hinge.symptomatic median.symptomatic\n", "6378 0.5967794 1.3178093 1.7339007\n", "6379 0.5971832 0.8834075 1.2250055\n", "6610 0.5590164 0.8709924 1.2120716\n", "7491 0.5967794 1.0117441 1.4264344\n", "10637 1.1159757 2.3252452 2.9142421\n", "10660 0.6287137 0.6520720 0.6754304\n", " upper_hinge.symptomatic max.symptomatic mean.symptomatic anum.symptomatic\n", "6378 2.1597202 2.864892 1.710400 104\n", "6379 1.6268354 2.204870 1.270898 104\n", "6610 1.5474867 2.598533 1.250900 104\n", "7491 1.8672471 2.499236 1.464334 104\n", "10637 3.2844509 3.803807 2.803249 104\n", "10660 0.8765771 1.077724 0.793956 104\n", " num.asymptomatic min.asymptomatic lower_hinge.asymptomatic\n", "6378 72 0.6506792 1.145084\n", "6379 67 0.6506792 1.166204\n", "6610 75 0.6506792 1.178380\n", "7491 97 0.8401246 1.448974\n", "10637 126 1.6975043 2.913854\n", "10660 72 0.7841837 1.095688\n", " median.asymptomatic upper_hinge.asymptomatic max.asymptomatic\n", "6378 1.627204 2.186565 3.507824\n", "6379 1.610719 1.902714 3.055638\n", "6610 1.710747 2.372549 4.086112\n", "7491 1.983295 2.322850 3.002516\n", "10637 3.210745 3.507824 4.057303\n", "10660 1.285891 1.603425 2.488704\n", " mean.asymptomatic anum.asymptomatic\n", "6378 1.704861 129\n", "6379 1.638683 129\n", "6610 1.845611 129\n", "7491 1.917958 129\n", "10637 3.175560 129\n", "10660 1.364373 129\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "'select()' returned 1:1 mapping between keys and columns\n", "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[1] \"Unresolved gene symbols\"\n", "character(0)\n", " gene p_val avg_log2FC pct.1 pct.2 p_val_adj num.symptomatic\n", "6378 IFI44L 4.170671e-07 -0.9870061 0.212 0.558 1.526507e-02 22\n", "6379 IFI6 8.671057e-07 -1.0030088 0.269 0.519 3.173693e-02 28\n", "6610 ISG15 4.491263e-07 -1.4168055 0.375 0.581 1.643847e-02 39\n", "7491 LY6E 5.056386e-11 -1.1795152 0.442 0.752 1.850688e-06 46\n", "10637 RPL23 5.288338e-07 -0.5722454 0.885 0.977 1.935585e-02 92\n", "10660 RPL39L 3.914921e-21 -1.4306520 0.029 0.558 1.432900e-16 3\n", " min.symptomatic lower_hinge.symptomatic median.symptomatic\n", "6378 0.5967794 1.3178093 1.7339007\n", "6379 0.5971832 0.8834075 1.2250055\n", "6610 0.5590164 0.8709924 1.2120716\n", "7491 0.5967794 1.0117441 1.4264344\n", "10637 1.1159757 2.3252452 2.9142421\n", "10660 0.6287137 0.6520720 0.6754304\n", " upper_hinge.symptomatic max.symptomatic mean.symptomatic anum.symptomatic\n", "6378 2.1597202 2.864892 1.710400 104\n", "6379 1.6268354 2.204870 1.270898 104\n", "6610 1.5474867 2.598533 1.250900 104\n", "7491 1.8672471 2.499236 1.464334 104\n", "10637 3.2844509 3.803807 2.803249 104\n", "10660 0.8765771 1.077724 0.793956 104\n", " num.asymptomatic min.asymptomatic lower_hinge.asymptomatic\n", "6378 72 0.6506792 1.145084\n", "6379 67 0.6506792 1.166204\n", "6610 75 0.6506792 1.178380\n", "7491 97 0.8401246 1.448974\n", "10637 126 1.6975043 2.913854\n", "10660 72 0.7841837 1.095688\n", " median.asymptomatic upper_hinge.asymptomatic max.asymptomatic\n", "6378 1.627204 2.186565 3.507824\n", "6379 1.610719 1.902714 3.055638\n", "6610 1.710747 2.372549 4.086112\n", "7491 1.983295 2.322850 3.002516\n", "10637 3.210745 3.507824 4.057303\n", "10660 1.285891 1.603425 2.488704\n", " mean.asymptomatic anum.asymptomatic\n", "6378 1.704861 129\n", "6379 1.638683 129\n", "6610 1.845611 129\n", "7491 1.917958 129\n", "10637 3.175560 129\n", "10660 1.364373 129\n", "[1] \"Got geneRegVec\"\n", "[1] 9\n", "[1] \"UpGenes 0\"\n", "[1] \"downGenes 9\"\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "--> No gene can be mapped....\n", "\n", "--> Expected input gene ID: 50856,5715,6133,5692,89869,3834\n", "\n", "--> return NULL...\n", "\n", "Reading KEGG annotation online: \"https://rest.kegg.jp/link/hsa/pathway\"...\n", "\n", "Reading KEGG annotation online: \"https://rest.kegg.jp/list/pathway/hsa\"...\n", "\n", "--> No gene can be mapped....\n", "\n", "--> Expected input gene ID: 2584,3033,2821,2592,5631,55586\n", "\n", "--> return NULL...\n", "\n", "--> No gene can be mapped....\n", "\n", "--> Expected input gene ID: 440138,7515,9488,5932,84899,29954\n", "\n", "--> return NULL...\n", "\n", "Reading KEGG annotation online: \"https://rest.kegg.jp/link/hsa/module\"...\n", "\n", "Reading KEGG annotation online: \"https://rest.kegg.jp/list/module\"...\n", "\n", "--> No gene can be mapped....\n", "\n", "--> Expected input gene ID: 5211,2539,22934,84076,9563,2271\n", "\n", "--> return NULL...\n", "\n", "--> No gene can be mapped....\n", "\n", "--> Expected input gene ID: 226,7167,6389,7086,3419,5232\n", "\n", "--> return NULL...\n", "\n", "--> No gene can be mapped....\n", "\n", "--> Expected input gene ID: 2597,3098,3101,3099,1738,5214\n", "\n", "--> return NULL...\n", "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[1] \"8\"\n", "[1] \"Selecting Genes\"\n", " gene p_val avg_log2FC pct.1 pct.2 p_val_adj num.symptomatic\n", "21 AATK 1.142941e-08 0.5071548 0.342 0.017 4.183279e-04 54\n", "23 ABCA1 2.648697e-23 1.0361618 0.652 0.068 9.694496e-19 103\n", "53 ABCG1 4.063203e-09 0.2654199 0.222 0.000 1.487173e-04 35\n", "64 ABHD17C 6.906994e-17 0.7545158 0.437 0.000 2.528029e-12 69\n", "169 AC005224.2 5.337825e-07 0.2554960 0.165 0.000 1.953697e-02 26\n", "580 AC016831.1 1.190837e-06 0.3525102 0.335 0.068 4.358583e-02 53\n", " min.symptomatic lower_hinge.symptomatic median.symptomatic\n", "21 0.3402284 0.5789649 0.7474997\n", "23 0.3637482 0.6338518 0.9481876\n", "53 0.3491913 0.4446892 0.5789649\n", "64 0.3595699 0.5448344 0.7574768\n", "169 0.3491913 0.5204474 0.7421677\n", "580 0.3402284 0.5025644 0.6706798\n", " upper_hinge.symptomatic max.symptomatic mean.symptomatic anum.symptomatic\n", "21 1.0590989 1.705198 0.8381763 158\n", "23 1.2858727 1.781008 0.9827799 158\n", "53 0.7613751 1.164052 0.6240979 158\n", "64 1.0590989 2.113924 0.8550206 158\n", "169 0.9166209 1.246548 0.7466593 158\n", "580 0.8320998 1.431438 0.7028730 158\n", " num.asymptomatic min.asymptomatic lower_hinge.asymptomatic\n", "21 1 1.5003163 1.5003163\n", "23 4 0.4894586 0.5167515\n", "53 NA NA NA\n", "64 NA NA NA\n", "169 NA NA NA\n", "580 4 0.5443176 0.5805683\n", " median.asymptomatic upper_hinge.asymptomatic max.asymptomatic\n", "21 1.5003163 1.5003163 1.5003163\n", "23 0.7860265 1.0413184 1.0546281\n", "53 NA NA NA\n", "64 NA NA NA\n", "169 NA NA NA\n", "580 0.7145006 0.8296829 0.8471836\n", " mean.asymptomatic anum.asymptomatic\n", "21 1.5003163 59\n", "23 0.7790349 59\n", "53 NA NA\n", "64 NA NA\n", "169 NA NA\n", "580 0.7051256 59\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "'select()' returned 1:1 mapping between keys and columns\n", "\n", "Warning message in clusterProfiler::bitr(siggenes$gene, fromType = \"SYMBOL\", toType = \"ENTREZID\", :\n", "\"2.9% of input gene IDs are fail to map...\"\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[1] \"Unresolved gene symbols\"\n", "[1] \"AC005224.2\" \"AC016831.1\" \"AC020656.1\" \"AC104530.1\" \"FAM214B\" \n", "[6] \"TMEM189\" \n", " gene p_val avg_log2FC pct.1 pct.2 p_val_adj num.symptomatic\n", "21 AATK 1.142941e-08 0.5071548 0.342 0.017 4.183279e-04 54\n", "23 ABCA1 2.648697e-23 1.0361618 0.652 0.068 9.694496e-19 103\n", "53 ABCG1 4.063203e-09 0.2654199 0.222 0.000 1.487173e-04 35\n", "64 ABHD17C 6.906994e-17 0.7545158 0.437 0.000 2.528029e-12 69\n", "1621 ADM 1.101965e-06 0.6535126 0.525 0.169 4.033301e-02 83\n", "1691 AGO2 3.149664e-10 0.7938171 0.722 0.288 1.152809e-05 114\n", " min.symptomatic lower_hinge.symptomatic median.symptomatic\n", "21 0.3402284 0.5789649 0.7474997\n", "23 0.3637482 0.6338518 0.9481876\n", "53 0.3491913 0.4446892 0.5789649\n", "64 0.3595699 0.5448344 0.7574768\n", "1621 0.2814861 0.6399542 0.9525674\n", "1691 0.3402284 0.6847054 0.9449351\n", " upper_hinge.symptomatic max.symptomatic mean.symptomatic anum.symptomatic\n", "21 1.0590989 1.705198 0.8381763 158\n", "23 1.2858727 1.781008 0.9827799 158\n", "53 0.7613751 1.164052 0.6240979 158\n", "64 1.0590989 2.113924 0.8550206 158\n", "1621 1.2932640 2.173482 1.0123646 158\n", "1691 1.2571553 2.300854 1.0119511 158\n", " num.asymptomatic min.asymptomatic lower_hinge.asymptomatic\n", "21 1 1.5003163 1.5003163\n", "23 4 0.4894586 0.5167515\n", "53 NA NA NA\n", "64 NA NA NA\n", "1621 10 0.3981513 0.6260296\n", "1691 17 0.3981513 0.6611566\n", " median.asymptomatic upper_hinge.asymptomatic max.asymptomatic\n", "21 1.5003163 1.500316 1.500316\n", "23 0.7860265 1.041318 1.054628\n", "53 NA NA NA\n", "64 NA NA NA\n", "1621 1.0291986 1.384348 1.500316\n", "1691 0.7913045 1.111237 1.323041\n", " mean.asymptomatic anum.asymptomatic\n", "21 1.5003163 59\n", "23 0.7790349 59\n", "53 NA NA\n", "64 NA NA\n", "1621 0.9898108 59\n", "1691 0.8586732 59\n", "[1] \"Got geneRegVec\"\n", "[1] 201\n", "[1] \"UpGenes 152\"\n", "[1] \"downGenes 49\"\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Reading KEGG annotation online: \"https://rest.kegg.jp/link/hsa/pathway\"...\n", "\n", "Reading KEGG annotation online: \"https://rest.kegg.jp/list/pathway/hsa\"...\n", "\n", "Reading KEGG annotation online: \"https://rest.kegg.jp/link/hsa/module\"...\n", "\n", "Reading KEGG annotation online: \"https://rest.kegg.jp/list/module\"...\n", "\n", "No gene sets have size between 10 and 500 ...\n", "\n", "--> return NULL...\n", "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[1] \"9\"\n", "[1] \"Selecting Genes\"\n", " gene p_val avg_log2FC pct.1 pct.2 p_val_adj num.symptomatic\n", "4 A2M-AS1 2.789253e-07 0.4145398 0.138 0 1.020894e-02 27\n", "7 AAGAB 5.524360e-07 0.3325795 0.128 0 2.021971e-02 25\n", "54 ABHD13 2.225306e-07 0.3372619 0.133 0 8.144841e-03 26\n", "59 ABHD16A 9.587160e-10 0.5225595 0.185 0 3.508996e-05 36\n", "117 AC004865.2 3.436087e-08 0.4398051 0.154 0 1.257642e-03 30\n", "138 AC005332.4 6.082116e-07 0.3470336 0.128 0 2.226115e-02 25\n", " min.symptomatic lower_hinge.symptomatic median.symptomatic\n", "4 0.6587183 0.9301740 1.0047784\n", "7 0.6266376 0.9555114 0.9946833\n", "54 0.8054829 0.9535466 1.0206043\n", "59 0.5621129 1.0169419 1.1498167\n", "117 0.8244270 0.9666778 1.0946367\n", "138 0.6587183 0.9672839 1.0745383\n", " upper_hinge.symptomatic max.symptomatic mean.symptomatic anum.symptomatic\n", "4 1.486996 1.855623 1.162793 195\n", "7 1.168847 1.730714 1.071343 195\n", "54 1.182686 1.499235 1.072151 195\n", "59 1.281405 2.073992 1.174755 195\n", "117 1.180597 1.994044 1.155179 195\n", "138 1.188155 1.730714 1.098095 195\n", " num.asymptomatic min.asymptomatic lower_hinge.asymptomatic\n", "4 NA NA NA\n", "7 NA NA NA\n", "54 NA NA NA\n", "59 NA NA NA\n", "117 NA NA NA\n", "138 NA NA NA\n", " median.asymptomatic upper_hinge.asymptomatic max.asymptomatic\n", "4 NA NA NA\n", "7 NA NA NA\n", "54 NA NA NA\n", "59 NA NA NA\n", "117 NA NA NA\n", "138 NA NA NA\n", " mean.asymptomatic anum.asymptomatic\n", "4 NA NA\n", "7 NA NA\n", "54 NA NA\n", "59 NA NA\n", "117 NA NA\n", "138 NA NA\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "'select()' returned 1:1 mapping between keys and columns\n", "\n", "Warning message in clusterProfiler::bitr(siggenes$gene, fromType = \"SYMBOL\", toType = \"ENTREZID\", :\n", "\"2.29% of input gene IDs are fail to map...\"\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[1] \"Unresolved gene symbols\"\n", " [1] \"AC004865.2\" \"AC005332.4\" \"C12orf65\" \"C19orf48\" \"CCDC130\" \n", " [6] \"DARS\" \"ELMSAN1\" \"FAM126B\" \"FAM192A\" \"FAM207A\" \n", "[11] \"H2AFX\" \"HNRNPA1P48\" \"IARS\" \"KARS\" \"LINC00476\" \n", "[16] \"MFSD14C\" \"MT-ND6\" \"TARS\" \"TMEM8A\" \"YARS\" \n", " gene p_val avg_log2FC pct.1 pct.2 p_val_adj num.symptomatic\n", "4 A2M-AS1 2.789253e-07 0.4145398 0.138 0 1.020894e-02 27\n", "7 AAGAB 5.524360e-07 0.3325795 0.128 0 2.021971e-02 25\n", "54 ABHD13 2.225306e-07 0.3372619 0.133 0 8.144841e-03 26\n", "59 ABHD16A 9.587160e-10 0.5225595 0.185 0 3.508996e-05 36\n", "1085 ACAP2 2.334492e-20 1.1288169 0.390 0 8.544475e-16 76\n", "1092 ACBD6 1.457872e-09 0.4445166 0.179 0 5.335958e-05 35\n", " min.symptomatic lower_hinge.symptomatic median.symptomatic\n", "4 0.6587183 0.9301740 1.0047784\n", "7 0.6266376 0.9555114 0.9946833\n", "54 0.8054829 0.9535466 1.0206043\n", "59 0.5621129 1.0169419 1.1498167\n", "1085 0.7593470 0.9855411 1.2306714\n", "1092 0.6266376 0.9337550 1.0511053\n", " upper_hinge.symptomatic max.symptomatic mean.symptomatic anum.symptomatic\n", "4 1.486996 1.855623 1.162793 195\n", "7 1.168847 1.730714 1.071343 195\n", "54 1.182686 1.499235 1.072151 195\n", "59 1.281405 2.073992 1.174755 195\n", "1085 1.500996 2.758967 1.304159 195\n", "1092 1.169085 1.705751 1.076814 195\n", " num.asymptomatic min.asymptomatic lower_hinge.asymptomatic\n", "4 NA NA NA\n", "7 NA NA NA\n", "54 NA NA NA\n", "59 NA NA NA\n", "1085 NA NA NA\n", "1092 NA NA NA\n", " median.asymptomatic upper_hinge.asymptomatic max.asymptomatic\n", "4 NA NA NA\n", "7 NA NA NA\n", "54 NA NA NA\n", "59 NA NA NA\n", "1085 NA NA NA\n", "1092 NA NA NA\n", " mean.asymptomatic anum.asymptomatic\n", "4 NA NA\n", "7 NA NA\n", "54 NA NA\n", "59 NA NA\n", "1085 NA NA\n", "1092 NA NA\n", "[1] \"Got geneRegVec\"\n", "[1] 853\n", "[1] \"UpGenes 853\"\n", "[1] \"downGenes 0\"\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "--> No gene can be mapped....\n", "\n", "--> Expected input gene ID: 161,64801,7388,10393,2939,10632\n", "\n", "--> return NULL...\n", "\n", "Reading KEGG annotation online: \"https://rest.kegg.jp/link/hsa/pathway\"...\n", "\n", "Reading KEGG annotation online: \"https://rest.kegg.jp/list/pathway/hsa\"...\n", "\n", "--> No gene can be mapped....\n", "\n", "--> Expected input gene ID: 79071,8789,57016,5236,6785,8277\n", "\n", "--> return NULL...\n", "\n", "--> No gene can be mapped....\n", "\n", "--> Expected input gene ID: 29954,10585,2779,9488,93183,85365\n", "\n", "--> return NULL...\n", "\n", "Reading KEGG annotation online: \"https://rest.kegg.jp/link/hsa/module\"...\n", "\n", "Reading KEGG annotation online: \"https://rest.kegg.jp/list/module\"...\n", "\n", "--> No gene can be mapped....\n", "\n", "--> Expected input gene ID: 3098,729020,5313,8277,55753,22934\n", "\n", "--> return NULL...\n", "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[1] \"10\"\n", "[1] \"Selecting Genes\"\n", " gene p_val avg_log2FC pct.1 pct.2 p_val_adj num.symptomatic\n", "1441 AP2B1 1.294620e-06 0.7383587 0.478 0 0.0473843749 22\n", "1731 ATP2A2 2.486289e-07 0.9642327 0.500 0 0.0091000650 23\n", "2289 CASP8 9.190416e-09 0.9289323 0.609 0 0.0003363784 28\n", "2504 CD58 3.365464e-08 1.1109301 0.565 0 0.0012317934 26\n", "3071 CSF2RB 1.875435e-07 0.8571122 0.522 0 0.0068642781 24\n", "4891 HELZ 1.152464e-07 0.9254556 0.587 0 0.0042181345 27\n", " min.symptomatic lower_hinge.symptomatic median.symptomatic\n", "1441 0.3574524 0.5866866 0.7702242\n", "1731 0.4436458 0.7702242 0.9733906\n", "2289 0.4614067 0.6237883 0.7733185\n", "2504 0.4439662 0.7498852 1.0006628\n", "3071 0.4292137 0.6854821 0.8683582\n", "4891 0.3574524 0.5058595 0.7316542\n", " upper_hinge.symptomatic max.symptomatic mean.symptomatic anum.symptomatic\n", "1441 0.8928866 1.706304 0.8115541 46\n", "1731 1.1285900 1.973851 1.0028764 46\n", "2289 0.9331386 2.076124 0.8392298 46\n", "2504 1.3664523 1.665239 1.0401941 46\n", "3071 0.9380692 1.926952 0.8740962 46\n", "4891 0.9649393 2.076124 0.8235565 46\n", " num.asymptomatic min.asymptomatic lower_hinge.asymptomatic\n", "1441 NA NA NA\n", "1731 NA NA NA\n", "2289 NA NA NA\n", "2504 NA NA NA\n", "3071 NA NA NA\n", "4891 NA NA NA\n", " median.asymptomatic upper_hinge.asymptomatic max.asymptomatic\n", "1441 NA NA NA\n", "1731 NA NA NA\n", "2289 NA NA NA\n", "2504 NA NA NA\n", "3071 NA NA NA\n", "4891 NA NA NA\n", " mean.asymptomatic anum.asymptomatic\n", "1441 NA NA\n", "1731 NA NA\n", "2289 NA NA\n", "2504 NA NA\n", "3071 NA NA\n", "4891 NA NA\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "'select()' returned 1:1 mapping between keys and columns\n", "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[1] \"Unresolved gene symbols\"\n", "character(0)\n", " gene p_val avg_log2FC pct.1 pct.2 p_val_adj num.symptomatic\n", "1441 AP2B1 1.294620e-06 0.7383587 0.478 0 0.0473843749 22\n", "1731 ATP2A2 2.486289e-07 0.9642327 0.500 0 0.0091000650 23\n", "2289 CASP8 9.190416e-09 0.9289323 0.609 0 0.0003363784 28\n", "2504 CD58 3.365464e-08 1.1109301 0.565 0 0.0012317934 26\n", "3071 CSF2RB 1.875435e-07 0.8571122 0.522 0 0.0068642781 24\n", "4891 HELZ 1.152464e-07 0.9254556 0.587 0 0.0042181345 27\n", " min.symptomatic lower_hinge.symptomatic median.symptomatic\n", "1441 0.3574524 0.5866866 0.7702242\n", "1731 0.4436458 0.7702242 0.9733906\n", "2289 0.4614067 0.6237883 0.7733185\n", "2504 0.4439662 0.7498852 1.0006628\n", "3071 0.4292137 0.6854821 0.8683582\n", "4891 0.3574524 0.5058595 0.7316542\n", " upper_hinge.symptomatic max.symptomatic mean.symptomatic anum.symptomatic\n", "1441 0.8928866 1.706304 0.8115541 46\n", "1731 1.1285900 1.973851 1.0028764 46\n", "2289 0.9331386 2.076124 0.8392298 46\n", "2504 1.3664523 1.665239 1.0401941 46\n", "3071 0.9380692 1.926952 0.8740962 46\n", "4891 0.9649393 2.076124 0.8235565 46\n", " num.asymptomatic min.asymptomatic lower_hinge.asymptomatic\n", "1441 NA NA NA\n", "1731 NA NA NA\n", "2289 NA NA NA\n", "2504 NA NA NA\n", "3071 NA NA NA\n", "4891 NA NA NA\n", " median.asymptomatic upper_hinge.asymptomatic max.asymptomatic\n", "1441 NA NA NA\n", "1731 NA NA NA\n", "2289 NA NA NA\n", "2504 NA NA NA\n", "3071 NA NA NA\n", "4891 NA NA NA\n", " mean.asymptomatic anum.asymptomatic\n", "1441 NA NA\n", "1731 NA NA\n", "2289 NA NA\n", "2504 NA NA\n", "3071 NA NA\n", "4891 NA NA\n", "[1] \"Got geneRegVec\"\n", "[1] 16\n", "[1] \"UpGenes 16\"\n", "[1] \"downGenes 0\"\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "--> No gene can be mapped....\n", "\n", "--> Expected input gene ID: 5919,6158,4728,55454,39,10382\n", "\n", "--> return NULL...\n", "\n", "Reading KEGG annotation online: \"https://rest.kegg.jp/link/hsa/pathway\"...\n", "\n", "Reading KEGG annotation online: \"https://rest.kegg.jp/list/pathway/hsa\"...\n", "\n", "--> No gene can be mapped....\n", "\n", "--> Expected input gene ID: 32,3948,83440,7360,729920,387712\n", "\n", "--> return NULL...\n", "\n", "--> No gene can be mapped....\n", "\n", "--> Expected input gene ID: 286148,10111,2072,56052,147991,84899\n", "\n", "--> return NULL...\n", "\n", "Reading KEGG annotation online: \"https://rest.kegg.jp/link/hsa/module\"...\n", "\n", "Reading KEGG annotation online: \"https://rest.kegg.jp/list/module\"...\n", "\n", "--> No gene can be mapped....\n", "\n", "--> Expected input gene ID: 5106,6391,441531,2203,221823,6389\n", "\n", "--> return NULL...\n", "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[1] \"11\"\n", "[1] \"Selecting Genes\"\n", " gene p_val avg_log2FC pct.1 pct.2 p_val_adj num.symptomatic\n", "1358 AKNA 2.121399e-07 1.1988011 0.696 0.250 0.007764534 55\n", "1863 ANKRD13D 4.850569e-07 0.8270597 0.456 0.036 0.017753567 36\n", "1916 AOAH 3.824880e-07 0.6542739 0.342 0.036 0.013999445 27\n", "2286 ATG5 9.140214e-07 0.6598790 0.494 0.071 0.033454099 39\n", "3272 CDC42SE1 4.099697e-07 0.9884886 0.709 0.179 0.015005299 56\n", "3347 CEBPB 9.674558e-07 1.2395118 0.671 0.179 0.035409851 53\n", " min.symptomatic lower_hinge.symptomatic median.symptomatic\n", "1358 0.1546643 0.7711902 1.1999666\n", "1863 0.1546643 0.5823801 0.8858112\n", "1916 0.7257719 0.8139281 0.9595171\n", "2286 0.2455944 0.5575162 0.7275372\n", "3272 0.1546643 0.8185632 1.0268173\n", "3347 0.1546643 0.7882174 1.0621231\n", " upper_hinge.symptomatic max.symptomatic mean.symptomatic anum.symptomatic\n", "1358 1.7077751 2.695448 1.2469044 79\n", "1863 1.1573268 2.281880 0.9161807 79\n", "1916 1.0660226 1.740205 0.9972050 79\n", "2286 0.9382489 1.723490 0.8094897 79\n", "3272 1.3513067 2.154143 1.1042628 79\n", "3347 1.7231117 2.882754 1.2215363 79\n", " num.asymptomatic min.asymptomatic lower_hinge.asymptomatic\n", "1358 7 0.4066687 0.6158581\n", "1863 1 0.8771483 0.8771483\n", "1916 1 0.5313994 0.5313994\n", "2286 2 0.7074999 0.7074999\n", "3272 5 0.3242090 1.0550246\n", "3347 5 0.6606358 0.7942841\n", " median.asymptomatic upper_hinge.asymptomatic max.asymptomatic\n", "1358 0.6729758 0.8683973 1.7884339\n", "1863 0.8771483 0.8771483 0.8771483\n", "1916 0.5313994 0.5313994 0.5313994\n", "2286 0.7508920 0.7942841 0.7942841\n", "3272 1.1051886 1.2499132 1.3558827\n", "3347 1.0402153 1.0716257 1.6323044\n", " mean.asymptomatic anum.asymptomatic\n", "1358 0.8337985 28\n", "1863 0.8771483 28\n", "1916 0.5313994 28\n", "2286 0.7508920 28\n", "3272 1.0180436 28\n", "3347 1.0398131 28\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "'select()' returned 1:1 mapping between keys and columns\n", "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[1] \"Unresolved gene symbols\"\n", "character(0)\n", " gene p_val avg_log2FC pct.1 pct.2 p_val_adj num.symptomatic\n", "1358 AKNA 2.121399e-07 1.1988011 0.696 0.250 0.007764534 55\n", "1863 ANKRD13D 4.850569e-07 0.8270597 0.456 0.036 0.017753567 36\n", "1916 AOAH 3.824880e-07 0.6542739 0.342 0.036 0.013999445 27\n", "2286 ATG5 9.140214e-07 0.6598790 0.494 0.071 0.033454099 39\n", "3272 CDC42SE1 4.099697e-07 0.9884886 0.709 0.179 0.015005299 56\n", "3347 CEBPB 9.674558e-07 1.2395118 0.671 0.179 0.035409851 53\n", " min.symptomatic lower_hinge.symptomatic median.symptomatic\n", "1358 0.1546643 0.7711902 1.1999666\n", "1863 0.1546643 0.5823801 0.8858112\n", "1916 0.7257719 0.8139281 0.9595171\n", "2286 0.2455944 0.5575162 0.7275372\n", "3272 0.1546643 0.8185632 1.0268173\n", "3347 0.1546643 0.7882174 1.0621231\n", " upper_hinge.symptomatic max.symptomatic mean.symptomatic anum.symptomatic\n", "1358 1.7077751 2.695448 1.2469044 79\n", "1863 1.1573268 2.281880 0.9161807 79\n", "1916 1.0660226 1.740205 0.9972050 79\n", "2286 0.9382489 1.723490 0.8094897 79\n", "3272 1.3513067 2.154143 1.1042628 79\n", "3347 1.7231117 2.882754 1.2215363 79\n", " num.asymptomatic min.asymptomatic lower_hinge.asymptomatic\n", "1358 7 0.4066687 0.6158581\n", "1863 1 0.8771483 0.8771483\n", "1916 1 0.5313994 0.5313994\n", "2286 2 0.7074999 0.7074999\n", "3272 5 0.3242090 1.0550246\n", "3347 5 0.6606358 0.7942841\n", " median.asymptomatic upper_hinge.asymptomatic max.asymptomatic\n", "1358 0.6729758 0.8683973 1.7884339\n", "1863 0.8771483 0.8771483 0.8771483\n", "1916 0.5313994 0.5313994 0.5313994\n", "2286 0.7508920 0.7942841 0.7942841\n", "3272 1.1051886 1.2499132 1.3558827\n", "3347 1.0402153 1.0716257 1.6323044\n", " mean.asymptomatic anum.asymptomatic\n", "1358 0.8337985 28\n", "1863 0.8771483 28\n", "1916 0.5313994 28\n", "2286 0.7508920 28\n", "3272 1.0180436 28\n", "3347 1.0398131 28\n", "[1] \"Got geneRegVec\"\n", "[1] 38\n", "[1] \"UpGenes 38\"\n", "[1] \"downGenes 0\"\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "--> No gene can be mapped....\n", "\n", "--> Expected input gene ID: 8639,55819,51129,1962,961,51000\n", "\n", "--> return NULL...\n", "\n", "Reading KEGG annotation online: \"https://rest.kegg.jp/link/hsa/pathway\"...\n", "\n", "Reading KEGG annotation online: \"https://rest.kegg.jp/list/pathway/hsa\"...\n", "\n", "--> No gene can be mapped....\n", "\n", "--> Expected input gene ID: 3099,729920,23205,51181,6391,9374\n", "\n", "--> return NULL...\n", "\n", "--> No gene can be mapped....\n", "\n", "--> Expected input gene ID: 29954,644974,3938,64421,79796,80235\n", "\n", "--> return NULL...\n", "\n", "Reading KEGG annotation online: \"https://rest.kegg.jp/link/hsa/module\"...\n", "\n", "Reading KEGG annotation online: \"https://rest.kegg.jp/list/module\"...\n", "\n", "--> No gene can be mapped....\n", "\n", "--> Expected input gene ID: 55753,1738,221823,84076,2821,5105\n", "\n", "--> return NULL...\n", "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[1] \"12\"\n", "[1] \"Selecting Genes\"\n", " gene p_val avg_log2FC pct.1 pct.2 p_val_adj num.symptomatic\n", "914 AHCYL1 6.431683e-07 0.7131734 0.731 0.000 0.023540601 19\n", "5020 HLA-DQA2 2.435797e-07 2.5639517 0.769 0.000 0.008915262 20\n", "5026 HLA-DRB5 4.052898e-08 3.1418799 0.808 0.125 0.001483401 21\n", "5811 LGALS2 1.796839e-07 -1.6754251 0.615 1.000 0.006576610 16\n", "10227 SULT1A1 7.701278e-07 0.8794845 0.731 0.000 0.028187446 19\n", " min.symptomatic lower_hinge.symptomatic median.symptomatic\n", "914 0.2784409 0.4179970 0.5422004\n", "5020 0.3074064 1.1519978 1.9998771\n", "5026 0.3074064 1.9151132 2.1971123\n", "5811 0.3513605 0.6595996 0.8078989\n", "10227 0.2868677 0.4955685 0.7160327\n", " upper_hinge.symptomatic max.symptomatic mean.symptomatic anum.symptomatic\n", "914 0.7815381 1.201691 0.5999268 26\n", "5020 2.2897792 2.859796 1.7805038 26\n", "5026 2.8476993 3.286919 2.2174381 26\n", "5811 1.2142680 1.690897 0.9249303 26\n", "10227 0.9478267 1.186374 0.7228356 26\n", " num.asymptomatic min.asymptomatic lower_hinge.asymptomatic\n", "914 NA NA NA\n", "5020 NA NA NA\n", "5026 1 0.5169532 0.5169532\n", "5811 8 1.3992772 1.5553922\n", "10227 NA NA NA\n", " median.asymptomatic upper_hinge.asymptomatic max.asymptomatic\n", "914 NA NA NA\n", "5020 NA NA NA\n", "5026 0.5169532 0.5169532 0.5169532\n", "5811 1.8742768 2.0544846 2.3988049\n", "10227 NA NA NA\n", " mean.asymptomatic anum.asymptomatic\n", "914 NA NA\n", "5020 NA NA\n", "5026 0.5169532 8\n", "5811 1.8457987 8\n", "10227 NA NA\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "'select()' returned 1:1 mapping between keys and columns\n", "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[1] \"Unresolved gene symbols\"\n", "character(0)\n", " gene p_val avg_log2FC pct.1 pct.2 p_val_adj num.symptomatic\n", "914 AHCYL1 6.431683e-07 0.7131734 0.731 0.000 0.023540601 19\n", "5020 HLA-DQA2 2.435797e-07 2.5639517 0.769 0.000 0.008915262 20\n", "5026 HLA-DRB5 4.052898e-08 3.1418799 0.808 0.125 0.001483401 21\n", "5811 LGALS2 1.796839e-07 -1.6754251 0.615 1.000 0.006576610 16\n", "10227 SULT1A1 7.701278e-07 0.8794845 0.731 0.000 0.028187446 19\n", " min.symptomatic lower_hinge.symptomatic median.symptomatic\n", "914 0.2784409 0.4179970 0.5422004\n", "5020 0.3074064 1.1519978 1.9998771\n", "5026 0.3074064 1.9151132 2.1971123\n", "5811 0.3513605 0.6595996 0.8078989\n", "10227 0.2868677 0.4955685 0.7160327\n", " upper_hinge.symptomatic max.symptomatic mean.symptomatic anum.symptomatic\n", "914 0.7815381 1.201691 0.5999268 26\n", "5020 2.2897792 2.859796 1.7805038 26\n", "5026 2.8476993 3.286919 2.2174381 26\n", "5811 1.2142680 1.690897 0.9249303 26\n", "10227 0.9478267 1.186374 0.7228356 26\n", " num.asymptomatic min.asymptomatic lower_hinge.asymptomatic\n", "914 NA NA NA\n", "5020 NA NA NA\n", "5026 1 0.5169532 0.5169532\n", "5811 8 1.3992772 1.5553922\n", "10227 NA NA NA\n", " median.asymptomatic upper_hinge.asymptomatic max.asymptomatic\n", "914 NA NA NA\n", "5020 NA NA NA\n", "5026 0.5169532 0.5169532 0.5169532\n", "5811 1.8742768 2.0544846 2.3988049\n", "10227 NA NA NA\n", " mean.asymptomatic anum.asymptomatic\n", "914 NA NA\n", "5020 NA NA\n", "5026 0.5169532 8\n", "5811 1.8457987 8\n", "10227 NA NA\n", "[1] \"Got geneRegVec\"\n", "[1] 5\n", "[1] \"UpGenes 4\"\n", "[1] \"downGenes 1\"\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "--> No gene can be mapped....\n", "\n", "--> Expected input gene ID: 8648,54675,2632,5165,56261,9896\n", "\n", "--> return NULL...\n", "\n", "Reading KEGG annotation online: \"https://rest.kegg.jp/link/hsa/pathway\"...\n", "\n", "Reading KEGG annotation online: \"https://rest.kegg.jp/list/pathway/hsa\"...\n", "\n", "--> No gene can be mapped....\n", "\n", "--> Expected input gene ID: 6652,10941,2717,37,2027,55556\n", "\n", "--> return NULL...\n", "\n", "Reading KEGG annotation online: \"https://rest.kegg.jp/link/hsa/module\"...\n", "\n", "Reading KEGG annotation online: \"https://rest.kegg.jp/list/module\"...\n", "\n", "No gene sets have size between 10 and 500 ...\n", "\n", "--> return NULL...\n", "\n", "No gene sets have size between 10 and 500 ...\n", "\n", "--> return NULL...\n", "\n", "--> No gene can be mapped....\n", "\n", "--> Expected input gene ID: 1743,22934,5634,2271,5230,3101\n", "\n", "--> return NULL...\n", "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[1] \"13\"\n", "[1] \"Selecting Genes\"\n", " [1] gene p_val avg_log2FC \n", " [4] pct.1 pct.2 p_val_adj \n", " [7] num.symptomatic min.symptomatic lower_hinge.symptomatic \n", "[10] median.symptomatic upper_hinge.symptomatic max.symptomatic \n", "[13] mean.symptomatic anum.symptomatic num.asymptomatic \n", "[16] min.asymptomatic lower_hinge.asymptomatic median.asymptomatic \n", "[19] upper_hinge.asymptomatic max.asymptomatic mean.asymptomatic \n", "[22] anum.asymptomatic \n", "<0 rows> (or 0-length row.names)\n", "[1] \"Unresolved gene symbols\"\n", "character(0)\n", " [1] gene p_val avg_log2FC \n", " [4] pct.1 pct.2 p_val_adj \n", " [7] num.symptomatic min.symptomatic lower_hinge.symptomatic \n", "[10] median.symptomatic upper_hinge.symptomatic max.symptomatic \n", "[13] mean.symptomatic anum.symptomatic num.asymptomatic \n", "[16] min.asymptomatic lower_hinge.asymptomatic median.asymptomatic \n", "[19] upper_hinge.asymptomatic max.asymptomatic mean.asymptomatic \n", "[22] anum.asymptomatic \n", "<0 rows> (or 0-length row.names)\n", "[1] \"Got geneRegVec\"\n", "[1] 0\n" ] } ], "source": [ "\n", "gseResults = performEnrichtmentAnalysis(obj.integrated, comparison.sympt_asympt, \"human\", \"./gse_results.rds\")\n", "\n" ] }, { "cell_type": "code", "execution_count": 42, "id": "0bfa8563", "metadata": { "lines_to_next_cell": 0 }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[1] \"0 rao\"\n", "[1] \"enrichment_plots//enrichment/0/rao/dotplot_30 6 12\"\n", "[1] \"Saving to file enrichment_plots//enrichment/0/rao/dotplot_30.png\"\n", "[1] \"Saving to file enrichment_plots//enrichment/0/rao/dotplot_30.pdf\"\n", "[1] \"Saving to file enrichment_plots//enrichment/0/rao/dotplot_30.svg\"\n", "[1] \"Saving to file enrichment_plots//enrichment/0/rao/dotplot_30.data\"\n", "[1] \"enrichment_plots//enrichment/0/rao/barplot_qvalue_30 6 12\"\n", "[1] \"Saving to file enrichment_plots//enrichment/0/rao/barplot_qvalue_30.png\"\n", "[1] \"Saving to file enrichment_plots//enrichment/0/rao/barplot_qvalue_30.pdf\"\n", "[1] \"Saving to file enrichment_plots//enrichment/0/rao/barplot_qvalue_30.svg\"\n", "[1] \"Saving to file enrichment_plots//enrichment/0/rao/barplot_qvalue_30.data\"\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "\u001b[1m\u001b[22mScale for \u001b[32msize\u001b[39m is already present.\n", "Adding another scale for \u001b[32msize\u001b[39m, which will replace the existing scale.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[1] \"enrichment_plots//enrichment/0/rao/cnetplot 12 12\"\n", "[1] \"Saving to file enrichment_plots//enrichment/0/rao/cnetplot.png\"\n", "[1] \"Saving to file enrichment_plots//enrichment/0/rao/cnetplot.pdf\"\n", "[1] \"Saving to file enrichment_plots//enrichment/0/rao/cnetplot.svg\"\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Warning message in treeplot.enrichResult(x, ...):\n", "\"Use 'cluster.params = list(n = your_value)' instead of 'nCluster'.\n", " The nCluster parameter will be removed in the next version.\"\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[1] \"enrichment_plots//enrichment/0/rao/treeplot 8 12\"\n", "[1] \"Saving to file enrichment_plots//enrichment/0/rao/treeplot.png\"\n", "[1] \"Saving to file enrichment_plots//enrichment/0/rao/treeplot.pdf\"\n", "[1] \"Saving to file enrichment_plots//enrichment/0/rao/treeplot.svg\"\n", "[1] \"enrichment_plots//enrichment/0/rao/emapplot 8 12\"\n", "[1] \"Saving to file enrichment_plots//enrichment/0/rao/emapplot.png\"\n", "[1] \"Saving to file enrichment_plots//enrichment/0/rao/emapplot.pdf\"\n", "[1] \"Saving to file enrichment_plots//enrichment/0/rao/emapplot.svg\"\n", "[1] \"0 rao_up\"\n", "[1] \"enrichment_plots//enrichment/0/rao_up/dotplot_30 6 12\"\n", "[1] \"Saving to file enrichment_plots//enrichment/0/rao_up/dotplot_30.png\"\n", "[1] \"Saving to file enrichment_plots//enrichment/0/rao_up/dotplot_30.pdf\"\n", "[1] \"Saving to file enrichment_plots//enrichment/0/rao_up/dotplot_30.svg\"\n", "[1] \"Saving to file enrichment_plots//enrichment/0/rao_up/dotplot_30.data\"\n", "[1] \"enrichment_plots//enrichment/0/rao_up/barplot_qvalue_30 6 12\"\n", "[1] \"Saving to file enrichment_plots//enrichment/0/rao_up/barplot_qvalue_30.png\"\n", "[1] \"Saving to file enrichment_plots//enrichment/0/rao_up/barplot_qvalue_30.pdf\"\n", "[1] \"Saving to file enrichment_plots//enrichment/0/rao_up/barplot_qvalue_30.svg\"\n", "[1] \"Saving to file enrichment_plots//enrichment/0/rao_up/barplot_qvalue_30.data\"\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "\u001b[1m\u001b[22mScale for \u001b[32msize\u001b[39m is already present.\n", "Adding another scale for \u001b[32msize\u001b[39m, which will replace the existing scale.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[1] \"enrichment_plots//enrichment/0/rao_up/cnetplot 12 12\"\n", "[1] \"Saving to file enrichment_plots//enrichment/0/rao_up/cnetplot.png\"\n", "[1] \"Saving to file enrichment_plots//enrichment/0/rao_up/cnetplot.pdf\"\n", "[1] \"Saving to file enrichment_plots//enrichment/0/rao_up/cnetplot.svg\"\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Warning message in treeplot.enrichResult(x, ...):\n", "\"Use 'cluster.params = list(n = your_value)' instead of 'nCluster'.\n", " The nCluster parameter will be removed in the next version.\"\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[1] \"enrichment_plots//enrichment/0/rao_up/treeplot 8 12\"\n", "[1] \"Saving to file enrichment_plots//enrichment/0/rao_up/treeplot.png\"\n", "[1] \"Saving to file enrichment_plots//enrichment/0/rao_up/treeplot.pdf\"\n", "[1] \"Saving to file enrichment_plots//enrichment/0/rao_up/treeplot.svg\"\n", "[1] \"enrichment_plots//enrichment/0/rao_up/emapplot 8 12\"\n", "[1] \"Saving to file enrichment_plots//enrichment/0/rao_up/emapplot.png\"\n", "[1] \"Saving to file enrichment_plots//enrichment/0/rao_up/emapplot.pdf\"\n", "[1] \"Saving to file enrichment_plots//enrichment/0/rao_up/emapplot.svg\"\n", "[1] \"0 rao_down\"\n", "[1] \"enrichment_plots//enrichment/0/rao_down/dotplot_30 6 12\"\n", "[1] \"Saving to file enrichment_plots//enrichment/0/rao_down/dotplot_30.png\"\n", "[1] \"Saving to file enrichment_plots//enrichment/0/rao_down/dotplot_30.pdf\"\n", "[1] \"Saving to file enrichment_plots//enrichment/0/rao_down/dotplot_30.svg\"\n", "[1] \"Saving to file enrichment_plots//enrichment/0/rao_down/dotplot_30.data\"\n", "[1] \"enrichment_plots//enrichment/0/rao_down/barplot_qvalue_30 6 12\"\n", "[1] \"Saving to file enrichment_plots//enrichment/0/rao_down/barplot_qvalue_30.png\"\n", "[1] \"Saving to file enrichment_plots//enrichment/0/rao_down/barplot_qvalue_30.pdf\"\n", "[1] \"Saving to file enrichment_plots//enrichment/0/rao_down/barplot_qvalue_30.svg\"\n", "[1] \"Saving to file enrichment_plots//enrichment/0/rao_down/barplot_qvalue_30.data\"\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "\u001b[1m\u001b[22mScale for \u001b[32msize\u001b[39m is already present.\n", "Adding another scale for \u001b[32msize\u001b[39m, which will replace the existing scale.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[1] \"enrichment_plots//enrichment/0/rao_down/cnetplot 12 12\"\n", "[1] \"Saving to file enrichment_plots//enrichment/0/rao_down/cnetplot.png\"\n", "[1] \"Saving to file enrichment_plots//enrichment/0/rao_down/cnetplot.pdf\"\n", "[1] \"Saving to file enrichment_plots//enrichment/0/rao_down/cnetplot.svg\"\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Warning message in treeplot.enrichResult(x, ...):\n", "\"Use 'cluster.params = list(n = your_value)' instead of 'nCluster'.\n", " The nCluster parameter will be removed in the 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enrichment_plots//enrichment/0/keggo/dotplot_30.svg\"\n", "[1] \"Saving to file enrichment_plots//enrichment/0/keggo/dotplot_30.data\"\n", "[1] \"enrichment_plots//enrichment/0/keggo/barplot_qvalue_30 6 12\"\n", "[1] \"Saving to file enrichment_plots//enrichment/0/keggo/barplot_qvalue_30.png\"\n", "[1] \"Saving to file enrichment_plots//enrichment/0/keggo/barplot_qvalue_30.pdf\"\n", "[1] \"Saving to file enrichment_plots//enrichment/0/keggo/barplot_qvalue_30.svg\"\n", "[1] \"Saving to file enrichment_plots//enrichment/0/keggo/barplot_qvalue_30.data\"\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "\u001b[1m\u001b[22mScale for \u001b[32msize\u001b[39m is already present.\n", "Adding another scale for \u001b[32msize\u001b[39m, which will replace the existing scale.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[1] \"enrichment_plots//enrichment/0/keggo/cnetplot 12 12\"\n", "[1] \"Saving to file enrichment_plots//enrichment/0/keggo/cnetplot.png\"\n", "[1] 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enrichment_plots//enrichment/0/goo/dotplot_30.data\"\n", "[1] \"enrichment_plots//enrichment/0/goo/barplot_qvalue_30 6 12\"\n", "[1] \"Saving to file enrichment_plots//enrichment/0/goo/barplot_qvalue_30.png\"\n", "[1] \"Saving to file enrichment_plots//enrichment/0/goo/barplot_qvalue_30.pdf\"\n", "[1] \"Saving to file enrichment_plots//enrichment/0/goo/barplot_qvalue_30.svg\"\n", "[1] \"Saving to file enrichment_plots//enrichment/0/goo/barplot_qvalue_30.data\"\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "\u001b[1m\u001b[22mScale for \u001b[32msize\u001b[39m is already present.\n", "Adding another scale for \u001b[32msize\u001b[39m, which will replace the existing scale.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[1] \"enrichment_plots//enrichment/0/goo/cnetplot 12 12\"\n", "[1] \"Saving to file enrichment_plots//enrichment/0/goo/cnetplot.png\"\n", "[1] \"Saving to file enrichment_plots//enrichment/0/goo/cnetplot.pdf\"\n", "[1] \"Saving to file enrichment_plots//enrichment/0/goo/cnetplot.svg\"\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Warning message in treeplot.enrichResult(x, ...):\n", "\"Use 'cluster.params = list(n = your_value)' instead of 'nCluster'.\n", " The nCluster parameter will be removed in the next version.\"\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[1] \"enrichment_plots//enrichment/0/goo/treeplot 8 12\"\n", "[1] \"Saving to file enrichment_plots//enrichment/0/goo/treeplot.png\"\n", "[1] \"Saving to file enrichment_plots//enrichment/0/goo/treeplot.pdf\"\n", "[1] \"Saving to file enrichment_plots//enrichment/0/goo/treeplot.svg\"\n", "[1] \"enrichment_plots//enrichment/0/goo/emapplot 8 12\"\n", "[1] \"Saving to file enrichment_plots//enrichment/0/goo/emapplot.png\"\n", "[1] \"Saving to file enrichment_plots//enrichment/0/goo/emapplot.pdf\"\n", "[1] \"Saving to file enrichment_plots//enrichment/0/goo/emapplot.svg\"\n", "[1] \"0 goo_up\"\n", "[1] 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enrichment_plots//enrichment/0/goo_up/treeplot.svg\"\n", "[1] \"enrichment_plots//enrichment/0/goo_up/emapplot 8 12\"\n", "[1] \"Saving to file enrichment_plots//enrichment/0/goo_up/emapplot.png\"\n", "[1] \"Saving to file enrichment_plots//enrichment/0/goo_up/emapplot.pdf\"\n", "[1] \"Saving to file enrichment_plots//enrichment/0/goo_up/emapplot.svg\"\n", "[1] \"0 goo_down\"\n", "[1] \"enrichment_plots//enrichment/0/goo_down/dotplot_30 6 12\"\n", "[1] \"Saving to file enrichment_plots//enrichment/0/goo_down/dotplot_30.png\"\n", "[1] \"Saving to file enrichment_plots//enrichment/0/goo_down/dotplot_30.pdf\"\n", "[1] \"Saving to file enrichment_plots//enrichment/0/goo_down/dotplot_30.svg\"\n", "[1] \"Saving to file enrichment_plots//enrichment/0/goo_down/dotplot_30.data\"\n", "[1] \"enrichment_plots//enrichment/0/goo_down/barplot_qvalue_30 6 12\"\n", "[1] \"Saving to file enrichment_plots//enrichment/0/goo_down/barplot_qvalue_30.png\"\n", "[1] \"Saving to file enrichment_plots//enrichment/0/goo_down/barplot_qvalue_30.pdf\"\n", "[1] \"Saving to file enrichment_plots//enrichment/0/goo_down/barplot_qvalue_30.svg\"\n", "[1] \"Saving to file enrichment_plots//enrichment/0/goo_down/barplot_qvalue_30.data\"\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "\u001b[1m\u001b[22mScale for \u001b[32msize\u001b[39m is already present.\n", "Adding another scale for \u001b[32msize\u001b[39m, which will replace the existing scale.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[1] \"enrichment_plots//enrichment/0/goo_down/cnetplot 12 12\"\n", "[1] \"Saving to file enrichment_plots//enrichment/0/goo_down/cnetplot.png\"\n", "[1] \"Saving to file enrichment_plots//enrichment/0/goo_down/cnetplot.pdf\"\n", "[1] \"Saving to file enrichment_plots//enrichment/0/goo_down/cnetplot.svg\"\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Warning message in treeplot.enrichResult(x, ...):\n", "\"Use 'cluster.params = list(n = your_value)' instead of 'nCluster'.\n", " The nCluster parameter will be removed in the next version.\"\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[1] \"enrichment_plots//enrichment/0/goo_down/treeplot 8 12\"\n", "[1] \"Saving to file enrichment_plots//enrichment/0/goo_down/treeplot.png\"\n", "[1] \"Saving to file enrichment_plots//enrichment/0/goo_down/treeplot.pdf\"\n", "[1] \"Saving to file enrichment_plots//enrichment/0/goo_down/treeplot.svg\"\n", "[1] \"enrichment_plots//enrichment/0/goo_down/emapplot 8 12\"\n", "[1] \"Saving to file enrichment_plots//enrichment/0/goo_down/emapplot.png\"\n", "[1] \"Saving to file enrichment_plots//enrichment/0/goo_down/emapplot.pdf\"\n", "[1] \"Saving to file enrichment_plots//enrichment/0/goo_down/emapplot.svg\"\n", "[1] \"1 rao\"\n", "[1] \"enrichment_plots//enrichment/1/rao/dotplot_30 6 12\"\n", "[1] \"Saving to file enrichment_plots//enrichment/1/rao/dotplot_30.png\"\n", "[1] \"Saving to file 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enrichment_plots//enrichment/1/rao/emapplot.pdf\"\n", "[1] \"Saving to file enrichment_plots//enrichment/1/rao/emapplot.svg\"\n", "[1] \"1 rao_up\"\n", "[1] \"enrichment_plots//enrichment/1/rao_up/dotplot_30 6 12\"\n", "[1] \"Saving to file enrichment_plots//enrichment/1/rao_up/dotplot_30.png\"\n", "[1] \"Saving to file enrichment_plots//enrichment/1/rao_up/dotplot_30.pdf\"\n", "[1] \"Saving to file enrichment_plots//enrichment/1/rao_up/dotplot_30.svg\"\n", "[1] \"Saving to file enrichment_plots//enrichment/1/rao_up/dotplot_30.data\"\n", "[1] \"enrichment_plots//enrichment/1/rao_up/barplot_qvalue_30 6 12\"\n", "[1] \"Saving to file enrichment_plots//enrichment/1/rao_up/barplot_qvalue_30.png\"\n", "[1] \"Saving to file enrichment_plots//enrichment/1/rao_up/barplot_qvalue_30.pdf\"\n", "[1] \"Saving to file enrichment_plots//enrichment/1/rao_up/barplot_qvalue_30.svg\"\n", "[1] \"Saving to file enrichment_plots//enrichment/1/rao_up/barplot_qvalue_30.data\"\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "\u001b[1m\u001b[22mScale for \u001b[32msize\u001b[39m is already present.\n", "Adding another scale for \u001b[32msize\u001b[39m, which will replace the existing scale.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[1] \"enrichment_plots//enrichment/1/rao_up/cnetplot 12 12\"\n", "[1] \"Saving to file enrichment_plots//enrichment/1/rao_up/cnetplot.png\"\n", "[1] \"Saving to file enrichment_plots//enrichment/1/rao_up/cnetplot.pdf\"\n", "[1] \"Saving to file enrichment_plots//enrichment/1/rao_up/cnetplot.svg\"\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Warning message in treeplot.enrichResult(x, ...):\n", "\"Use 'cluster.params = list(n = your_value)' instead of 'nCluster'.\n", " The nCluster parameter will be removed in the next version.\"\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[1] \"enrichment_plots//enrichment/1/rao_up/treeplot 8 12\"\n", "[1] \"Saving to file enrichment_plots//enrichment/1/rao_up/treeplot.png\"\n", "[1] \"Saving to file enrichment_plots//enrichment/1/rao_up/treeplot.pdf\"\n", "[1] \"Saving to file enrichment_plots//enrichment/1/rao_up/treeplot.svg\"\n", "[1] \"enrichment_plots//enrichment/1/rao_up/emapplot 8 12\"\n", "[1] \"Saving to file enrichment_plots//enrichment/1/rao_up/emapplot.png\"\n", "[1] \"Saving to file enrichment_plots//enrichment/1/rao_up/emapplot.pdf\"\n", "[1] \"Saving to file enrichment_plots//enrichment/1/rao_up/emapplot.svg\"\n", "[1] \"1 rao_down\"\n", "[1] \"enrichment_plots//enrichment/1/rao_down/dotplot_30 6 12\"\n", "[1] \"Saving to file enrichment_plots//enrichment/1/rao_down/dotplot_30.png\"\n", "[1] \"Saving to file enrichment_plots//enrichment/1/rao_down/dotplot_30.pdf\"\n", "[1] \"Saving to file enrichment_plots//enrichment/1/rao_down/dotplot_30.svg\"\n", "[1] \"Saving to file enrichment_plots//enrichment/1/rao_down/dotplot_30.data\"\n", "[1] 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enrichment_plots//enrichment/1/rao_down/cnetplot.svg\"\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Warning message in treeplot.enrichResult(x, ...):\n", "\"Use 'cluster.params = list(n = your_value)' instead of 'nCluster'.\n", " The nCluster parameter will be removed in the next version.\"\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[1] \"enrichment_plots//enrichment/1/rao_down/treeplot 8 12\"\n", "[1] \"Saving to file enrichment_plots//enrichment/1/rao_down/treeplot.png\"\n", "[1] \"Saving to file enrichment_plots//enrichment/1/rao_down/treeplot.pdf\"\n", "[1] \"Saving to file enrichment_plots//enrichment/1/rao_down/treeplot.svg\"\n", "[1] \"enrichment_plots//enrichment/1/rao_down/emapplot 8 12\"\n", "[1] \"Saving to file enrichment_plots//enrichment/1/rao_down/emapplot.png\"\n", "[1] \"Saving to file enrichment_plots//enrichment/1/rao_down/emapplot.pdf\"\n", "[1] \"Saving to file enrichment_plots//enrichment/1/rao_down/emapplot.svg\"\n", "[1] \"1 keggo\"\n", "[1] \"enrichment_plots//enrichment/1/keggo/dotplot_30 6 12\"\n", "[1] \"Saving to file enrichment_plots//enrichment/1/keggo/dotplot_30.png\"\n", "[1] \"Saving to file enrichment_plots//enrichment/1/keggo/dotplot_30.pdf\"\n", "[1] \"Saving to file enrichment_plots//enrichment/1/keggo/dotplot_30.svg\"\n", "[1] \"Saving to file enrichment_plots//enrichment/1/keggo/dotplot_30.data\"\n", "[1] \"enrichment_plots//enrichment/1/keggo/barplot_qvalue_30 6 12\"\n", "[1] \"Saving to file enrichment_plots//enrichment/1/keggo/barplot_qvalue_30.png\"\n", "[1] \"Saving to file enrichment_plots//enrichment/1/keggo/barplot_qvalue_30.pdf\"\n", "[1] \"Saving to file enrichment_plots//enrichment/1/keggo/barplot_qvalue_30.svg\"\n", "[1] \"Saving to file enrichment_plots//enrichment/1/keggo/barplot_qvalue_30.data\"\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "\u001b[1m\u001b[22mScale for \u001b[32msize\u001b[39m is already present.\n", "Adding another scale for 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enrichment_plots//enrichment/1/mkeggo_up/dotplot_30.data\"\n", "[1] \"enrichment_plots//enrichment/1/mkeggo_up/barplot_qvalue_30 6 12\"\n", "[1] \"Saving to file enrichment_plots//enrichment/1/mkeggo_up/barplot_qvalue_30.png\"\n", "[1] \"Saving to file enrichment_plots//enrichment/1/mkeggo_up/barplot_qvalue_30.pdf\"\n", "[1] \"Saving to file enrichment_plots//enrichment/1/mkeggo_up/barplot_qvalue_30.svg\"\n", "[1] \"Saving to file enrichment_plots//enrichment/1/mkeggo_up/barplot_qvalue_30.data\"\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Warning message in treeplot.enrichResult(x, ...):\n", "\"Use 'cluster.params = list(n = your_value)' instead of 'nCluster'.\n", " The nCluster parameter will be removed in the next version.\"\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[1] \"enrichment_plots//enrichment/1/mkeggo_up/treeplot 8 12\"\n", "[1] \"Saving to file enrichment_plots//enrichment/1/mkeggo_up/treeplot.png\"\n", "[1] \"Saving to file enrichment_plots//enrichment/1/mkeggo_up/treeplot.pdf\"\n", "[1] \"Saving to file enrichment_plots//enrichment/1/mkeggo_up/treeplot.svg\"\n", "[1] \"enrichment_plots//enrichment/1/mkeggo_up/emapplot 8 12\"\n", "[1] \"Saving to file enrichment_plots//enrichment/1/mkeggo_up/emapplot.png\"\n", "[1] \"Saving to file enrichment_plots//enrichment/1/mkeggo_up/emapplot.pdf\"\n", "[1] \"Saving to file enrichment_plots//enrichment/1/mkeggo_up/emapplot.svg\"\n", "[1] \"1 mkeggo_down\"\n", "[1] \"1 goo\"\n", "[1] \"enrichment_plots//enrichment/1/goo/dotplot_30 6 12\"\n", "[1] \"Saving to file enrichment_plots//enrichment/1/goo/dotplot_30.png\"\n", "[1] \"Saving to file enrichment_plots//enrichment/1/goo/dotplot_30.pdf\"\n", "[1] \"Saving to file enrichment_plots//enrichment/1/goo/dotplot_30.svg\"\n", "[1] \"Saving to file enrichment_plots//enrichment/1/goo/dotplot_30.data\"\n", "[1] \"enrichment_plots//enrichment/1/goo/barplot_qvalue_30 6 12\"\n", "[1] \"Saving to file enrichment_plots//enrichment/1/goo/barplot_qvalue_30.png\"\n", "[1] \"Saving to file enrichment_plots//enrichment/1/goo/barplot_qvalue_30.pdf\"\n", "[1] \"Saving to file enrichment_plots//enrichment/1/goo/barplot_qvalue_30.svg\"\n", "[1] \"Saving to file enrichment_plots//enrichment/1/goo/barplot_qvalue_30.data\"\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "\u001b[1m\u001b[22mScale for \u001b[32msize\u001b[39m is already present.\n", "Adding another scale for \u001b[32msize\u001b[39m, which will replace the existing scale.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[1] \"enrichment_plots//enrichment/1/goo/cnetplot 12 12\"\n", "[1] \"Saving to file enrichment_plots//enrichment/1/goo/cnetplot.png\"\n", "[1] \"Saving to file enrichment_plots//enrichment/1/goo/cnetplot.pdf\"\n", "[1] \"Saving to file enrichment_plots//enrichment/1/goo/cnetplot.svg\"\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Warning message in treeplot.enrichResult(x, ...):\n", "\"Use 'cluster.params = list(n = your_value)' instead of 'nCluster'.\n", " The nCluster parameter will be removed in the next version.\"\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[1] \"enrichment_plots//enrichment/1/goo/treeplot 8 12\"\n", "[1] \"Saving to file enrichment_plots//enrichment/1/goo/treeplot.png\"\n", "[1] \"Saving to file enrichment_plots//enrichment/1/goo/treeplot.pdf\"\n", "[1] \"Saving to file enrichment_plots//enrichment/1/goo/treeplot.svg\"\n", "[1] \"enrichment_plots//enrichment/1/goo/emapplot 8 12\"\n", "[1] \"Saving to file enrichment_plots//enrichment/1/goo/emapplot.png\"\n", "[1] \"Saving to file enrichment_plots//enrichment/1/goo/emapplot.pdf\"\n", "[1] \"Saving to file enrichment_plots//enrichment/1/goo/emapplot.svg\"\n", "[1] \"1 goo_up\"\n", "[1] \"enrichment_plots//enrichment/1/goo_up/dotplot_30 6 12\"\n", "[1] \"Saving to file enrichment_plots//enrichment/1/goo_up/dotplot_30.png\"\n", "[1] \"Saving 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file enrichment_plots//enrichment/1/goo_up/emapplot.png\"\n", "[1] \"Saving to file enrichment_plots//enrichment/1/goo_up/emapplot.pdf\"\n", "[1] \"Saving to file enrichment_plots//enrichment/1/goo_up/emapplot.svg\"\n", "[1] \"1 goo_down\"\n", "[1] \"enrichment_plots//enrichment/1/goo_down/dotplot_30 6 12\"\n", "[1] \"Saving to file enrichment_plots//enrichment/1/goo_down/dotplot_30.png\"\n", "[1] \"Saving to file enrichment_plots//enrichment/1/goo_down/dotplot_30.pdf\"\n", "[1] \"Saving to file enrichment_plots//enrichment/1/goo_down/dotplot_30.svg\"\n", "[1] \"Saving to file enrichment_plots//enrichment/1/goo_down/dotplot_30.data\"\n", "[1] \"enrichment_plots//enrichment/1/goo_down/barplot_qvalue_30 6 12\"\n", "[1] \"Saving to file enrichment_plots//enrichment/1/goo_down/barplot_qvalue_30.png\"\n", "[1] \"Saving to file enrichment_plots//enrichment/1/goo_down/barplot_qvalue_30.pdf\"\n", "[1] \"Saving to file enrichment_plots//enrichment/1/goo_down/barplot_qvalue_30.svg\"\n", "[1] \"Saving to file enrichment_plots//enrichment/1/goo_down/barplot_qvalue_30.data\"\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "\u001b[1m\u001b[22mScale for \u001b[32msize\u001b[39m is already present.\n", "Adding another scale for \u001b[32msize\u001b[39m, which will replace the existing scale.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[1] \"enrichment_plots//enrichment/1/goo_down/cnetplot 12 12\"\n", "[1] \"Saving to file enrichment_plots//enrichment/1/goo_down/cnetplot.png\"\n", "[1] \"Saving to file enrichment_plots//enrichment/1/goo_down/cnetplot.pdf\"\n", "[1] \"Saving to file enrichment_plots//enrichment/1/goo_down/cnetplot.svg\"\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Warning message in treeplot.enrichResult(x, ...):\n", "\"Use 'cluster.params = list(n = your_value)' instead of 'nCluster'.\n", " The nCluster parameter will be removed in the next version.\"\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[1] \"enrichment_plots//enrichment/1/goo_down/treeplot 8 12\"\n", "[1] \"Saving to file enrichment_plots//enrichment/1/goo_down/treeplot.png\"\n", "[1] \"Saving to file enrichment_plots//enrichment/1/goo_down/treeplot.pdf\"\n", "[1] \"Saving to file enrichment_plots//enrichment/1/goo_down/treeplot.svg\"\n", "[1] \"enrichment_plots//enrichment/1/goo_down/emapplot 8 12\"\n", "[1] \"Saving to file enrichment_plots//enrichment/1/goo_down/emapplot.png\"\n", "[1] \"Saving to file enrichment_plots//enrichment/1/goo_down/emapplot.pdf\"\n", "[1] \"Saving to file enrichment_plots//enrichment/1/goo_down/emapplot.svg\"\n", "[1] \"2 rao\"\n", "[1] \"enrichment_plots//enrichment/2/rao/dotplot_30 6 12\"\n", "[1] \"Saving to file enrichment_plots//enrichment/2/rao/dotplot_30.png\"\n", "[1] \"Saving to file enrichment_plots//enrichment/2/rao/dotplot_30.pdf\"\n", "[1] \"Saving to file enrichment_plots//enrichment/2/rao/dotplot_30.svg\"\n", "[1] \"Saving to file enrichment_plots//enrichment/2/rao/dotplot_30.data\"\n", "[1] \"enrichment_plots//enrichment/2/rao/barplot_qvalue_30 6 12\"\n", "[1] \"Saving to file enrichment_plots//enrichment/2/rao/barplot_qvalue_30.png\"\n", "[1] \"Saving to file enrichment_plots//enrichment/2/rao/barplot_qvalue_30.pdf\"\n", "[1] \"Saving to file enrichment_plots//enrichment/2/rao/barplot_qvalue_30.svg\"\n", "[1] \"Saving to file enrichment_plots//enrichment/2/rao/barplot_qvalue_30.data\"\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "\u001b[1m\u001b[22mScale for \u001b[32msize\u001b[39m is already present.\n", "Adding another scale for \u001b[32msize\u001b[39m, which will replace the existing scale.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[1] \"enrichment_plots//enrichment/2/rao/cnetplot 12 12\"\n", "[1] \"Saving to file enrichment_plots//enrichment/2/rao/cnetplot.png\"\n", "[1] \"Saving to file enrichment_plots//enrichment/2/rao/cnetplot.pdf\"\n", "[1] \"Saving to file enrichment_plots//enrichment/2/rao/cnetplot.svg\"\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Warning message in treeplot.enrichResult(x, ...):\n", "\"Use 'cluster.params = list(n = your_value)' instead of 'nCluster'.\n", " The nCluster parameter will be removed in the next version.\"\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[1] \"enrichment_plots//enrichment/2/rao/treeplot 8 12\"\n", "[1] \"Saving to file enrichment_plots//enrichment/2/rao/treeplot.png\"\n", "[1] \"Saving to file enrichment_plots//enrichment/2/rao/treeplot.pdf\"\n", "[1] \"Saving to file enrichment_plots//enrichment/2/rao/treeplot.svg\"\n", "[1] \"enrichment_plots//enrichment/2/rao/emapplot 8 12\"\n", "[1] \"Saving to file enrichment_plots//enrichment/2/rao/emapplot.png\"\n", "[1] \"Saving to file enrichment_plots//enrichment/2/rao/emapplot.pdf\"\n", "[1] \"Saving to file enrichment_plots//enrichment/2/rao/emapplot.svg\"\n", "[1] \"2 rao_up\"\n", "[1] 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enrichment_plots//enrichment/2/rao_up/treeplot.svg\"\n", "[1] \"enrichment_plots//enrichment/2/rao_up/emapplot 8 12\"\n", "[1] \"Saving to file enrichment_plots//enrichment/2/rao_up/emapplot.png\"\n", "[1] \"Saving to file enrichment_plots//enrichment/2/rao_up/emapplot.pdf\"\n", "[1] \"Saving to file enrichment_plots//enrichment/2/rao_up/emapplot.svg\"\n", "[1] \"2 rao_down\"\n", "[1] \"enrichment_plots//enrichment/2/rao_down/dotplot_30 6 12\"\n", "[1] \"Saving to file enrichment_plots//enrichment/2/rao_down/dotplot_30.png\"\n", "[1] \"Saving to file enrichment_plots//enrichment/2/rao_down/dotplot_30.pdf\"\n", "[1] \"Saving to file enrichment_plots//enrichment/2/rao_down/dotplot_30.svg\"\n", "[1] \"Saving to file enrichment_plots//enrichment/2/rao_down/dotplot_30.data\"\n", "[1] \"enrichment_plots//enrichment/2/rao_down/barplot_qvalue_30 6 12\"\n", "[1] \"Saving to file enrichment_plots//enrichment/2/rao_down/barplot_qvalue_30.png\"\n", "[1] \"Saving to file enrichment_plots//enrichment/2/rao_down/barplot_qvalue_30.pdf\"\n", "[1] \"Saving to file enrichment_plots//enrichment/2/rao_down/barplot_qvalue_30.svg\"\n", "[1] \"Saving to file enrichment_plots//enrichment/2/rao_down/barplot_qvalue_30.data\"\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "\u001b[1m\u001b[22mScale for \u001b[32msize\u001b[39m is already present.\n", "Adding another scale for \u001b[32msize\u001b[39m, which will replace the existing scale.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[1] \"enrichment_plots//enrichment/2/rao_down/cnetplot 12 12\"\n", "[1] \"Saving to file enrichment_plots//enrichment/2/rao_down/cnetplot.png\"\n", "[1] \"Saving to file enrichment_plots//enrichment/2/rao_down/cnetplot.pdf\"\n", "[1] \"Saving to file enrichment_plots//enrichment/2/rao_down/cnetplot.svg\"\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Warning message in treeplot.enrichResult(x, ...):\n", "\"Use 'cluster.params = list(n = your_value)' instead of 'nCluster'.\n", " The nCluster parameter will be removed in the next version.\"\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[1] \"enrichment_plots//enrichment/2/rao_down/treeplot 8 12\"\n", "[1] \"Saving to file enrichment_plots//enrichment/2/rao_down/treeplot.png\"\n", "[1] \"Saving to file enrichment_plots//enrichment/2/rao_down/treeplot.pdf\"\n", "[1] \"Saving to file enrichment_plots//enrichment/2/rao_down/treeplot.svg\"\n", "[1] \"enrichment_plots//enrichment/2/rao_down/emapplot 8 12\"\n", "[1] \"Saving to file enrichment_plots//enrichment/2/rao_down/emapplot.png\"\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Warning message:\n", "\"ggrepel: 2 unlabeled data points (too many overlaps). Consider increasing max.overlaps\"\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[1] \"Saving to file enrichment_plots//enrichment/2/rao_down/emapplot.pdf\"\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Warning message:\n", "\"ggrepel: 2 unlabeled data points (too many overlaps). Consider increasing max.overlaps\"\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[1] \"Saving to file enrichment_plots//enrichment/2/rao_down/emapplot.svg\"\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Warning message:\n", "\"ggrepel: 2 unlabeled data points (too many overlaps). Consider increasing max.overlaps\"\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[1] \"2 keggo\"\n", "[1] \"enrichment_plots//enrichment/2/keggo/dotplot_30 6 12\"\n", "[1] \"Saving to file enrichment_plots//enrichment/2/keggo/dotplot_30.png\"\n", "[1] \"Saving to file enrichment_plots//enrichment/2/keggo/dotplot_30.pdf\"\n", "[1] \"Saving to file enrichment_plots//enrichment/2/keggo/dotplot_30.svg\"\n", "[1] \"Saving to file enrichment_plots//enrichment/2/keggo/dotplot_30.data\"\n", "[1] \"enrichment_plots//enrichment/2/keggo/barplot_qvalue_30 6 12\"\n", "[1] \"Saving to file enrichment_plots//enrichment/2/keggo/barplot_qvalue_30.png\"\n", "[1] \"Saving to file enrichment_plots//enrichment/2/keggo/barplot_qvalue_30.pdf\"\n", "[1] \"Saving to file enrichment_plots//enrichment/2/keggo/barplot_qvalue_30.svg\"\n", "[1] \"Saving to file enrichment_plots//enrichment/2/keggo/barplot_qvalue_30.data\"\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "\u001b[1m\u001b[22mScale for \u001b[32msize\u001b[39m is already present.\n", "Adding another scale for \u001b[32msize\u001b[39m, which will replace the existing scale.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[1] \"enrichment_plots//enrichment/2/keggo/cnetplot 12 12\"\n", "[1] \"Saving to file enrichment_plots//enrichment/2/keggo/cnetplot.png\"\n", "[1] \"Saving to file enrichment_plots//enrichment/2/keggo/cnetplot.pdf\"\n", "[1] \"Saving to file enrichment_plots//enrichment/2/keggo/cnetplot.svg\"\n", "[1] \"2 keggo_up\"\n", "[1] \"enrichment_plots//enrichment/2/keggo_up/dotplot_30 6 12\"\n", "[1] \"Saving to file enrichment_plots//enrichment/2/keggo_up/dotplot_30.png\"\n", "[1] \"Saving to file enrichment_plots//enrichment/2/keggo_up/dotplot_30.pdf\"\n", "[1] \"Saving to file enrichment_plots//enrichment/2/keggo_up/dotplot_30.svg\"\n", "[1] \"Saving to file enrichment_plots//enrichment/2/keggo_up/dotplot_30.data\"\n", "[1] 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enrichment_plots//enrichment/2/keggo_up/cnetplot.svg\"\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Warning message in treeplot.enrichResult(x, ...):\n", "\"Use 'cluster.params = list(n = your_value)' instead of 'nCluster'.\n", " The nCluster parameter will be removed in the next version.\"\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[1] \"enrichment_plots//enrichment/2/keggo_up/treeplot 8 12\"\n", "[1] \"Saving to file enrichment_plots//enrichment/2/keggo_up/treeplot.png\"\n", "[1] \"Saving to file enrichment_plots//enrichment/2/keggo_up/treeplot.pdf\"\n", "[1] \"Saving to file enrichment_plots//enrichment/2/keggo_up/treeplot.svg\"\n", "[1] \"enrichment_plots//enrichment/2/keggo_up/emapplot 8 12\"\n", "[1] \"Saving to file enrichment_plots//enrichment/2/keggo_up/emapplot.png\"\n", "[1] \"Saving to file enrichment_plots//enrichment/2/keggo_up/emapplot.pdf\"\n", "[1] \"Saving to file enrichment_plots//enrichment/2/keggo_up/emapplot.svg\"\n", "[1] \"2 keggo_down\"\n", "[1] \"enrichment_plots//enrichment/2/keggo_down/dotplot_30 6 12\"\n", "[1] \"Saving to file enrichment_plots//enrichment/2/keggo_down/dotplot_30.png\"\n", "[1] \"Saving to file enrichment_plots//enrichment/2/keggo_down/dotplot_30.pdf\"\n", "[1] \"Saving to file enrichment_plots//enrichment/2/keggo_down/dotplot_30.svg\"\n", "[1] \"Saving to file enrichment_plots//enrichment/2/keggo_down/dotplot_30.data\"\n", "[1] \"enrichment_plots//enrichment/2/keggo_down/barplot_qvalue_30 6 12\"\n", "[1] \"Saving to file enrichment_plots//enrichment/2/keggo_down/barplot_qvalue_30.png\"\n", "[1] \"Saving to file enrichment_plots//enrichment/2/keggo_down/barplot_qvalue_30.pdf\"\n", "[1] \"Saving to file enrichment_plots//enrichment/2/keggo_down/barplot_qvalue_30.svg\"\n", "[1] \"Saving to file enrichment_plots//enrichment/2/keggo_down/barplot_qvalue_30.data\"\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "\u001b[1m\u001b[22mScale for \u001b[32msize\u001b[39m is already present.\n", "Adding another scale for \u001b[32msize\u001b[39m, which will replace the existing scale.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[1] \"enrichment_plots//enrichment/2/keggo_down/cnetplot 12 12\"\n", "[1] \"Saving to file enrichment_plots//enrichment/2/keggo_down/cnetplot.png\"\n", "[1] \"Saving to file enrichment_plots//enrichment/2/keggo_down/cnetplot.pdf\"\n", "[1] \"Saving to file enrichment_plots//enrichment/2/keggo_down/cnetplot.svg\"\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Warning message in treeplot.enrichResult(x, ...):\n", "\"Use 'cluster.params = list(n = your_value)' instead of 'nCluster'.\n", " The nCluster parameter will be removed in the next version.\"\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[1] \"enrichment_plots//enrichment/2/keggo_down/treeplot 8 12\"\n", "[1] \"Saving to file enrichment_plots//enrichment/2/keggo_down/treeplot.png\"\n", "[1] \"Saving to file enrichment_plots//enrichment/2/keggo_down/treeplot.pdf\"\n", "[1] \"Saving to file enrichment_plots//enrichment/2/keggo_down/treeplot.svg\"\n", "[1] \"enrichment_plots//enrichment/2/keggo_down/emapplot 8 12\"\n", "[1] \"Saving to file enrichment_plots//enrichment/2/keggo_down/emapplot.png\"\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Warning message:\n", "\"ggrepel: 9 unlabeled data points (too many overlaps). Consider increasing max.overlaps\"\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[1] \"Saving to file enrichment_plots//enrichment/2/keggo_down/emapplot.pdf\"\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Warning message:\n", "\"ggrepel: 8 unlabeled data points (too many overlaps). Consider increasing max.overlaps\"\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[1] \"Saving to file enrichment_plots//enrichment/2/keggo_down/emapplot.svg\"\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Warning message:\n", "\"ggrepel: 8 unlabeled data points (too many overlaps). Consider increasing max.overlaps\"\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[1] \"2 mkeggo\"\n", "[1] \"2 mkeggo_up\"\n", "[1] \"2 mkeggo_down\"\n", "[1] \"2 goo\"\n", "[1] \"enrichment_plots//enrichment/2/goo/dotplot_30 6 12\"\n", "[1] \"Saving to file enrichment_plots//enrichment/2/goo/dotplot_30.png\"\n", "[1] \"Saving to file enrichment_plots//enrichment/2/goo/dotplot_30.pdf\"\n", "[1] \"Saving to file enrichment_plots//enrichment/2/goo/dotplot_30.svg\"\n", "[1] \"Saving to file enrichment_plots//enrichment/2/goo/dotplot_30.data\"\n", "[1] \"enrichment_plots//enrichment/2/goo/barplot_qvalue_30 6 12\"\n", "[1] \"Saving to file enrichment_plots//enrichment/2/goo/barplot_qvalue_30.png\"\n", "[1] \"Saving to file enrichment_plots//enrichment/2/goo/barplot_qvalue_30.pdf\"\n", "[1] \"Saving to file enrichment_plots//enrichment/2/goo/barplot_qvalue_30.svg\"\n", "[1] \"Saving to file enrichment_plots//enrichment/2/goo/barplot_qvalue_30.data\"\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "\u001b[1m\u001b[22mScale for \u001b[32msize\u001b[39m is already present.\n", "Adding another scale for \u001b[32msize\u001b[39m, which will replace the existing scale.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[1] \"enrichment_plots//enrichment/2/goo/cnetplot 12 12\"\n", "[1] \"Saving to file enrichment_plots//enrichment/2/goo/cnetplot.png\"\n", "[1] \"Saving to file enrichment_plots//enrichment/2/goo/cnetplot.pdf\"\n", "[1] \"Saving to file enrichment_plots//enrichment/2/goo/cnetplot.svg\"\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Warning message in treeplot.enrichResult(x, ...):\n", "\"Use 'cluster.params = list(n = your_value)' instead of 'nCluster'.\n", " The nCluster parameter will be removed in the next version.\"\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[1] \"enrichment_plots//enrichment/2/goo/treeplot 8 12\"\n", "[1] \"Saving to file enrichment_plots//enrichment/2/goo/treeplot.png\"\n", "[1] \"Saving to file enrichment_plots//enrichment/2/goo/treeplot.pdf\"\n", "[1] \"Saving to file enrichment_plots//enrichment/2/goo/treeplot.svg\"\n", "[1] \"enrichment_plots//enrichment/2/goo/emapplot 8 12\"\n", "[1] \"Saving to file enrichment_plots//enrichment/2/goo/emapplot.png\"\n", "[1] \"Saving to file enrichment_plots//enrichment/2/goo/emapplot.pdf\"\n", "[1] \"Saving to file enrichment_plots//enrichment/2/goo/emapplot.svg\"\n", "[1] \"2 goo_up\"\n", "[1] \"enrichment_plots//enrichment/2/goo_up/dotplot_30 6 12\"\n", "[1] \"Saving to file enrichment_plots//enrichment/2/goo_up/dotplot_30.png\"\n", "[1] \"Saving to file enrichment_plots//enrichment/2/goo_up/dotplot_30.pdf\"\n", "[1] \"Saving to file enrichment_plots//enrichment/2/goo_up/dotplot_30.svg\"\n", "[1] \"Saving to file enrichment_plots//enrichment/2/goo_up/dotplot_30.data\"\n", "[1] \"enrichment_plots//enrichment/2/goo_up/barplot_qvalue_30 6 12\"\n", "[1] \"Saving to file enrichment_plots//enrichment/2/goo_up/barplot_qvalue_30.png\"\n", "[1] \"Saving to file enrichment_plots//enrichment/2/goo_up/barplot_qvalue_30.pdf\"\n", "[1] \"Saving to file enrichment_plots//enrichment/2/goo_up/barplot_qvalue_30.svg\"\n", "[1] \"Saving to file enrichment_plots//enrichment/2/goo_up/barplot_qvalue_30.data\"\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "\u001b[1m\u001b[22mScale for \u001b[32msize\u001b[39m is already present.\n", "Adding another scale for \u001b[32msize\u001b[39m, which will replace the existing scale.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[1] \"enrichment_plots//enrichment/2/goo_up/cnetplot 12 12\"\n", "[1] \"Saving to file enrichment_plots//enrichment/2/goo_up/cnetplot.png\"\n", "[1] \"Saving to file enrichment_plots//enrichment/2/goo_up/cnetplot.pdf\"\n", "[1] \"Saving to file enrichment_plots//enrichment/2/goo_up/cnetplot.svg\"\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Warning message in treeplot.enrichResult(x, ...):\n", "\"Use 'cluster.params = list(n = your_value)' instead of 'nCluster'.\n", " The nCluster parameter will be removed in the next version.\"\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[1] \"enrichment_plots//enrichment/2/goo_up/treeplot 8 12\"\n", "[1] \"Saving to file enrichment_plots//enrichment/2/goo_up/treeplot.png\"\n", "[1] \"Saving to file enrichment_plots//enrichment/2/goo_up/treeplot.pdf\"\n", "[1] \"Saving to file enrichment_plots//enrichment/2/goo_up/treeplot.svg\"\n", "[1] \"enrichment_plots//enrichment/2/goo_up/emapplot 8 12\"\n", "[1] \"Saving to file enrichment_plots//enrichment/2/goo_up/emapplot.png\"\n", "[1] \"Saving to file enrichment_plots//enrichment/2/goo_up/emapplot.pdf\"\n", "[1] \"Saving to file enrichment_plots//enrichment/2/goo_up/emapplot.svg\"\n", "[1] \"2 goo_down\"\n", "[1] \"enrichment_plots//enrichment/2/goo_down/dotplot_30 6 12\"\n", "[1] \"Saving to file enrichment_plots//enrichment/2/goo_down/dotplot_30.png\"\n", "[1] \"Saving to file enrichment_plots//enrichment/2/goo_down/dotplot_30.pdf\"\n", "[1] \"Saving to file enrichment_plots//enrichment/2/goo_down/dotplot_30.svg\"\n", "[1] \"Saving to file enrichment_plots//enrichment/2/goo_down/dotplot_30.data\"\n", "[1] \"enrichment_plots//enrichment/2/goo_down/barplot_qvalue_30 6 12\"\n", "[1] \"Saving to file enrichment_plots//enrichment/2/goo_down/barplot_qvalue_30.png\"\n", "[1] \"Saving to file enrichment_plots//enrichment/2/goo_down/barplot_qvalue_30.pdf\"\n", "[1] \"Saving to file enrichment_plots//enrichment/2/goo_down/barplot_qvalue_30.svg\"\n", "[1] \"Saving to file enrichment_plots//enrichment/2/goo_down/barplot_qvalue_30.data\"\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Warning message in treeplot.enrichResult(x, ...):\n", "\"Use 'cluster.params = list(n = your_value)' instead of 'nCluster'.\n", " The nCluster parameter will be removed in the next version.\"\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[1] \"enrichment_plots//enrichment/2/goo_down/treeplot 8 12\"\n", "[1] \"Saving to file enrichment_plots//enrichment/2/goo_down/treeplot.png\"\n", "[1] \"Saving to file enrichment_plots//enrichment/2/goo_down/treeplot.pdf\"\n", "[1] \"Saving to file enrichment_plots//enrichment/2/goo_down/treeplot.svg\"\n", "[1] \"enrichment_plots//enrichment/2/goo_down/emapplot 8 12\"\n", "[1] \"Saving to file enrichment_plots//enrichment/2/goo_down/emapplot.png\"\n", "[1] \"Saving to file enrichment_plots//enrichment/2/goo_down/emapplot.pdf\"\n", "[1] \"Saving to file enrichment_plots//enrichment/2/goo_down/emapplot.svg\"\n", "[1] \"3 rao\"\n", "[1] \"enrichment_plots//enrichment/3/rao/dotplot_30 6 12\"\n", "[1] \"Saving to file enrichment_plots//enrichment/3/rao/dotplot_30.png\"\n", "[1] \"Saving to file enrichment_plots//enrichment/3/rao/dotplot_30.pdf\"\n", "[1] \"Saving to file enrichment_plots//enrichment/3/rao/dotplot_30.svg\"\n", "[1] \"Saving to file enrichment_plots//enrichment/3/rao/dotplot_30.data\"\n", "[1] \"enrichment_plots//enrichment/3/rao/barplot_qvalue_30 6 12\"\n", "[1] \"Saving to file enrichment_plots//enrichment/3/rao/barplot_qvalue_30.png\"\n", "[1] \"Saving to file enrichment_plots//enrichment/3/rao/barplot_qvalue_30.pdf\"\n", "[1] \"Saving to file enrichment_plots//enrichment/3/rao/barplot_qvalue_30.svg\"\n", "[1] \"Saving to file enrichment_plots//enrichment/3/rao/barplot_qvalue_30.data\"\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "\u001b[1m\u001b[22mScale for \u001b[32msize\u001b[39m is already present.\n", "Adding another scale for \u001b[32msize\u001b[39m, which will replace the existing scale.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[1] \"enrichment_plots//enrichment/3/rao/cnetplot 12 12\"\n", "[1] \"Saving to file enrichment_plots//enrichment/3/rao/cnetplot.png\"\n", "[1] \"Saving to file enrichment_plots//enrichment/3/rao/cnetplot.pdf\"\n", "[1] \"Saving to file enrichment_plots//enrichment/3/rao/cnetplot.svg\"\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Warning message in treeplot.enrichResult(x, ...):\n", "\"Use 'cluster.params = list(n = your_value)' instead of 'nCluster'.\n", " The nCluster parameter will be removed in the next version.\"\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[1] \"enrichment_plots//enrichment/3/rao/treeplot 8 12\"\n", "[1] \"Saving to file enrichment_plots//enrichment/3/rao/treeplot.png\"\n", "[1] \"Saving to file enrichment_plots//enrichment/3/rao/treeplot.pdf\"\n", "[1] \"Saving to file enrichment_plots//enrichment/3/rao/treeplot.svg\"\n", "[1] \"enrichment_plots//enrichment/3/rao/emapplot 8 12\"\n", "[1] \"Saving to file enrichment_plots//enrichment/3/rao/emapplot.png\"\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Warning message:\n", "\"ggrepel: 2 unlabeled data points (too many overlaps). Consider increasing max.overlaps\"\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[1] \"Saving to file enrichment_plots//enrichment/3/rao/emapplot.pdf\"\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Warning message:\n", "\"ggrepel: 2 unlabeled data points (too many overlaps). Consider increasing max.overlaps\"\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[1] \"Saving to file enrichment_plots//enrichment/3/rao/emapplot.svg\"\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Warning message:\n", "\"ggrepel: 2 unlabeled data points (too many overlaps). Consider increasing max.overlaps\"\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[1] \"3 rao_up\"\n", "[1] \"enrichment_plots//enrichment/3/rao_up/dotplot_30 6 12\"\n", "[1] \"Saving to file enrichment_plots//enrichment/3/rao_up/dotplot_30.png\"\n", "[1] \"Saving to file enrichment_plots//enrichment/3/rao_up/dotplot_30.pdf\"\n", "[1] \"Saving to file enrichment_plots//enrichment/3/rao_up/dotplot_30.svg\"\n", "[1] \"Saving to file enrichment_plots//enrichment/3/rao_up/dotplot_30.data\"\n", "[1] \"enrichment_plots//enrichment/3/rao_up/barplot_qvalue_30 6 12\"\n", "[1] \"Saving to file enrichment_plots//enrichment/3/rao_up/barplot_qvalue_30.png\"\n", "[1] \"Saving to file enrichment_plots//enrichment/3/rao_up/barplot_qvalue_30.pdf\"\n", "[1] \"Saving to file enrichment_plots//enrichment/3/rao_up/barplot_qvalue_30.svg\"\n", "[1] \"Saving to file enrichment_plots//enrichment/3/rao_up/barplot_qvalue_30.data\"\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "\u001b[1m\u001b[22mScale for \u001b[32msize\u001b[39m is already present.\n", "Adding another scale for \u001b[32msize\u001b[39m, which will replace the existing scale.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[1] \"enrichment_plots//enrichment/3/rao_up/cnetplot 12 12\"\n", "[1] \"Saving to file enrichment_plots//enrichment/3/rao_up/cnetplot.png\"\n", "[1] \"Saving to file enrichment_plots//enrichment/3/rao_up/cnetplot.pdf\"\n", "[1] \"Saving to file enrichment_plots//enrichment/3/rao_up/cnetplot.svg\"\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Warning message in treeplot.enrichResult(x, ...):\n", "\"Use 'cluster.params = list(n = your_value)' instead of 'nCluster'.\n", " The nCluster parameter will be removed in the next version.\"\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[1] \"enrichment_plots//enrichment/3/rao_up/treeplot 8 12\"\n", "[1] \"Saving to file enrichment_plots//enrichment/3/rao_up/treeplot.png\"\n", "[1] \"Saving to file enrichment_plots//enrichment/3/rao_up/treeplot.pdf\"\n", "[1] \"Saving to file enrichment_plots//enrichment/3/rao_up/treeplot.svg\"\n", "[1] \"enrichment_plots//enrichment/3/rao_up/emapplot 8 12\"\n", "[1] \"Saving to file enrichment_plots//enrichment/3/rao_up/emapplot.png\"\n", "[1] \"Saving to file enrichment_plots//enrichment/3/rao_up/emapplot.pdf\"\n", "[1] \"Saving to file enrichment_plots//enrichment/3/rao_up/emapplot.svg\"\n", "[1] \"3 rao_down\"\n", "[1] \"enrichment_plots//enrichment/3/rao_down/dotplot_30 6 12\"\n", "[1] \"Saving to file enrichment_plots//enrichment/3/rao_down/dotplot_30.png\"\n", "[1] \"Saving to file enrichment_plots//enrichment/3/rao_down/dotplot_30.pdf\"\n", "[1] \"Saving to file enrichment_plots//enrichment/3/rao_down/dotplot_30.svg\"\n", "[1] \"Saving to file enrichment_plots//enrichment/3/rao_down/dotplot_30.data\"\n", "[1] \"enrichment_plots//enrichment/3/rao_down/barplot_qvalue_30 6 12\"\n", "[1] \"Saving to file enrichment_plots//enrichment/3/rao_down/barplot_qvalue_30.png\"\n", "[1] \"Saving to file enrichment_plots//enrichment/3/rao_down/barplot_qvalue_30.pdf\"\n", "[1] \"Saving to file enrichment_plots//enrichment/3/rao_down/barplot_qvalue_30.svg\"\n", "[1] \"Saving to file enrichment_plots//enrichment/3/rao_down/barplot_qvalue_30.data\"\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "\u001b[1m\u001b[22mScale for \u001b[32msize\u001b[39m is already present.\n", "Adding another scale for \u001b[32msize\u001b[39m, which will replace the existing scale.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[1] \"enrichment_plots//enrichment/3/rao_down/cnetplot 12 12\"\n", "[1] \"Saving to file enrichment_plots//enrichment/3/rao_down/cnetplot.png\"\n", "[1] \"Saving to file enrichment_plots//enrichment/3/rao_down/cnetplot.pdf\"\n", "[1] \"Saving to file enrichment_plots//enrichment/3/rao_down/cnetplot.svg\"\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Warning message in treeplot.enrichResult(x, ...):\n", "\"Use 'cluster.params = list(n = your_value)' instead of 'nCluster'.\n", " The nCluster parameter will be removed in the next version.\"\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[1] \"enrichment_plots//enrichment/3/rao_down/treeplot 8 12\"\n", "[1] \"Saving to file enrichment_plots//enrichment/3/rao_down/treeplot.png\"\n", "[1] \"Saving to file enrichment_plots//enrichment/3/rao_down/treeplot.pdf\"\n", "[1] \"Saving to file enrichment_plots//enrichment/3/rao_down/treeplot.svg\"\n", "[1] \"enrichment_plots//enrichment/3/rao_down/emapplot 8 12\"\n", "[1] \"Saving to file enrichment_plots//enrichment/3/rao_down/emapplot.png\"\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Warning message:\n", "\"ggrepel: 12 unlabeled data points (too many overlaps). Consider increasing max.overlaps\"\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[1] \"Saving to file enrichment_plots//enrichment/3/rao_down/emapplot.pdf\"\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Warning message:\n", "\"ggrepel: 12 unlabeled data points (too many overlaps). Consider increasing max.overlaps\"\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[1] \"Saving to file enrichment_plots//enrichment/3/rao_down/emapplot.svg\"\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Warning message:\n", "\"ggrepel: 12 unlabeled data points (too many overlaps). Consider increasing max.overlaps\"\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[1] \"3 keggo\"\n", "[1] \"enrichment_plots//enrichment/3/keggo/dotplot_30 6 12\"\n", "[1] \"Saving to file enrichment_plots//enrichment/3/keggo/dotplot_30.png\"\n", "[1] \"Saving to file enrichment_plots//enrichment/3/keggo/dotplot_30.pdf\"\n", "[1] \"Saving to file enrichment_plots//enrichment/3/keggo/dotplot_30.svg\"\n", "[1] \"Saving to file enrichment_plots//enrichment/3/keggo/dotplot_30.data\"\n", "[1] \"enrichment_plots//enrichment/3/keggo/barplot_qvalue_30 6 12\"\n", "[1] \"Saving to file enrichment_plots//enrichment/3/keggo/barplot_qvalue_30.png\"\n", "[1] \"Saving to file enrichment_plots//enrichment/3/keggo/barplot_qvalue_30.pdf\"\n", "[1] \"Saving to file enrichment_plots//enrichment/3/keggo/barplot_qvalue_30.svg\"\n", "[1] \"Saving to file enrichment_plots//enrichment/3/keggo/barplot_qvalue_30.data\"\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "\u001b[1m\u001b[22mScale for \u001b[32msize\u001b[39m is already present.\n", "Adding another scale for \u001b[32msize\u001b[39m, which will replace the existing scale.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[1] \"enrichment_plots//enrichment/3/keggo/cnetplot 12 12\"\n", "[1] \"Saving to file enrichment_plots//enrichment/3/keggo/cnetplot.png\"\n", "[1] \"Saving to file enrichment_plots//enrichment/3/keggo/cnetplot.pdf\"\n", "[1] \"Saving to file enrichment_plots//enrichment/3/keggo/cnetplot.svg\"\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Warning message in treeplot.enrichResult(x, ...):\n", "\"Use 'cluster.params = list(n = your_value)' instead of 'nCluster'.\n", " The nCluster parameter will be removed in the next version.\"\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[1] \"enrichment_plots//enrichment/3/keggo/treeplot 8 12\"\n", "[1] \"Saving to file enrichment_plots//enrichment/3/keggo/treeplot.png\"\n", "[1] 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enrichment_plots//enrichment/3/keggo_up/barplot_qvalue_30.png\"\n", "[1] \"Saving to file enrichment_plots//enrichment/3/keggo_up/barplot_qvalue_30.pdf\"\n", "[1] \"Saving to file enrichment_plots//enrichment/3/keggo_up/barplot_qvalue_30.svg\"\n", "[1] \"Saving to file enrichment_plots//enrichment/3/keggo_up/barplot_qvalue_30.data\"\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "\u001b[1m\u001b[22mScale for \u001b[32msize\u001b[39m is already present.\n", "Adding another scale for \u001b[32msize\u001b[39m, which will replace the existing scale.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[1] \"enrichment_plots//enrichment/3/keggo_up/cnetplot 12 12\"\n", "[1] \"Saving to file enrichment_plots//enrichment/3/keggo_up/cnetplot.png\"\n", "[1] \"Saving to file enrichment_plots//enrichment/3/keggo_up/cnetplot.pdf\"\n", "[1] \"Saving to file enrichment_plots//enrichment/3/keggo_up/cnetplot.svg\"\n", "[1] \"3 keggo_down\"\n", "[1] \"enrichment_plots//enrichment/3/keggo_down/dotplot_30 6 12\"\n", "[1] \"Saving to file enrichment_plots//enrichment/3/keggo_down/dotplot_30.png\"\n", "[1] \"Saving to file enrichment_plots//enrichment/3/keggo_down/dotplot_30.pdf\"\n", "[1] \"Saving to file enrichment_plots//enrichment/3/keggo_down/dotplot_30.svg\"\n", "[1] \"Saving to file enrichment_plots//enrichment/3/keggo_down/dotplot_30.data\"\n", "[1] \"enrichment_plots//enrichment/3/keggo_down/barplot_qvalue_30 6 12\"\n", "[1] \"Saving to file enrichment_plots//enrichment/3/keggo_down/barplot_qvalue_30.png\"\n", "[1] \"Saving to file enrichment_plots//enrichment/3/keggo_down/barplot_qvalue_30.pdf\"\n", "[1] \"Saving to file enrichment_plots//enrichment/3/keggo_down/barplot_qvalue_30.svg\"\n", "[1] \"Saving to file enrichment_plots//enrichment/3/keggo_down/barplot_qvalue_30.data\"\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "\u001b[1m\u001b[22mScale for \u001b[32msize\u001b[39m is already present.\n", "Adding another scale for \u001b[32msize\u001b[39m, which will replace the existing scale.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[1] \"enrichment_plots//enrichment/3/keggo_down/cnetplot 12 12\"\n", "[1] \"Saving to file enrichment_plots//enrichment/3/keggo_down/cnetplot.png\"\n", "[1] \"Saving to file enrichment_plots//enrichment/3/keggo_down/cnetplot.pdf\"\n", "[1] \"Saving to file enrichment_plots//enrichment/3/keggo_down/cnetplot.svg\"\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Warning message in treeplot.enrichResult(x, ...):\n", "\"Use 'cluster.params = list(n = your_value)' instead of 'nCluster'.\n", " The nCluster parameter will be removed in the next version.\"\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[1] \"enrichment_plots//enrichment/3/keggo_down/treeplot 8 12\"\n", "[1] \"Saving to file enrichment_plots//enrichment/3/keggo_down/treeplot.png\"\n", "[1] \"Saving to file enrichment_plots//enrichment/3/keggo_down/treeplot.pdf\"\n", "[1] \"Saving to file enrichment_plots//enrichment/3/keggo_down/treeplot.svg\"\n", "[1] \"enrichment_plots//enrichment/3/keggo_down/emapplot 8 12\"\n", "[1] \"Saving to file enrichment_plots//enrichment/3/keggo_down/emapplot.png\"\n", "[1] \"Saving to file enrichment_plots//enrichment/3/keggo_down/emapplot.pdf\"\n", "[1] \"Saving to file enrichment_plots//enrichment/3/keggo_down/emapplot.svg\"\n", "[1] \"3 mkeggo\"\n", "[1] \"3 mkeggo_up\"\n", "[1] \"3 mkeggo_down\"\n", "[1] \"3 goo\"\n", "[1] \"enrichment_plots//enrichment/3/goo/dotplot_30 6 12\"\n", "[1] \"Saving to file enrichment_plots//enrichment/3/goo/dotplot_30.png\"\n", "[1] \"Saving to file enrichment_plots//enrichment/3/goo/dotplot_30.pdf\"\n", "[1] \"Saving to file enrichment_plots//enrichment/3/goo/dotplot_30.svg\"\n", "[1] \"Saving to file enrichment_plots//enrichment/3/goo/dotplot_30.data\"\n", "[1] \"enrichment_plots//enrichment/3/goo/barplot_qvalue_30 6 12\"\n", "[1] \"Saving to file enrichment_plots//enrichment/3/goo/barplot_qvalue_30.png\"\n", "[1] \"Saving to file enrichment_plots//enrichment/3/goo/barplot_qvalue_30.pdf\"\n", "[1] \"Saving to file enrichment_plots//enrichment/3/goo/barplot_qvalue_30.svg\"\n", "[1] \"Saving to file enrichment_plots//enrichment/3/goo/barplot_qvalue_30.data\"\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "\u001b[1m\u001b[22mScale for \u001b[32msize\u001b[39m is already present.\n", "Adding another scale for \u001b[32msize\u001b[39m, which will replace the existing scale.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[1] \"enrichment_plots//enrichment/3/goo/cnetplot 12 12\"\n", "[1] \"Saving to file enrichment_plots//enrichment/3/goo/cnetplot.png\"\n", "[1] \"Saving to file enrichment_plots//enrichment/3/goo/cnetplot.pdf\"\n", "[1] \"Saving to file enrichment_plots//enrichment/3/goo/cnetplot.svg\"\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Warning message in treeplot.enrichResult(x, ...):\n", "\"Use 'cluster.params = list(n = your_value)' instead of 'nCluster'.\n", " The nCluster parameter will be removed in the next version.\"\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[1] \"enrichment_plots//enrichment/3/goo/treeplot 8 12\"\n", "[1] \"Saving to file enrichment_plots//enrichment/3/goo/treeplot.png\"\n", "[1] \"Saving to file enrichment_plots//enrichment/3/goo/treeplot.pdf\"\n", "[1] \"Saving to file enrichment_plots//enrichment/3/goo/treeplot.svg\"\n", "[1] \"enrichment_plots//enrichment/3/goo/emapplot 8 12\"\n", "[1] \"Saving to file enrichment_plots//enrichment/3/goo/emapplot.png\"\n", "[1] \"Saving to file enrichment_plots//enrichment/3/goo/emapplot.pdf\"\n", "[1] \"Saving to file enrichment_plots//enrichment/3/goo/emapplot.svg\"\n", "[1] \"3 goo_up\"\n", "[1] \"enrichment_plots//enrichment/3/goo_up/dotplot_30 6 12\"\n", "[1] \"Saving to file enrichment_plots//enrichment/3/goo_up/dotplot_30.png\"\n", "[1] \"Saving to file enrichment_plots//enrichment/3/goo_up/dotplot_30.pdf\"\n", "[1] \"Saving to file enrichment_plots//enrichment/3/goo_up/dotplot_30.svg\"\n", "[1] \"Saving to file enrichment_plots//enrichment/3/goo_up/dotplot_30.data\"\n", "[1] \"enrichment_plots//enrichment/3/goo_up/barplot_qvalue_30 6 12\"\n", "[1] \"Saving to file enrichment_plots//enrichment/3/goo_up/barplot_qvalue_30.png\"\n", "[1] \"Saving to file enrichment_plots//enrichment/3/goo_up/barplot_qvalue_30.pdf\"\n", "[1] \"Saving to file enrichment_plots//enrichment/3/goo_up/barplot_qvalue_30.svg\"\n", "[1] \"Saving to file enrichment_plots//enrichment/3/goo_up/barplot_qvalue_30.data\"\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "\u001b[1m\u001b[22mScale for \u001b[32msize\u001b[39m is already present.\n", "Adding another scale for \u001b[32msize\u001b[39m, which will replace the existing scale.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[1] \"enrichment_plots//enrichment/3/goo_up/cnetplot 12 12\"\n", "[1] \"Saving to file enrichment_plots//enrichment/3/goo_up/cnetplot.png\"\n", "[1] \"Saving to file enrichment_plots//enrichment/3/goo_up/cnetplot.pdf\"\n", "[1] \"Saving to file enrichment_plots//enrichment/3/goo_up/cnetplot.svg\"\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Warning message in treeplot.enrichResult(x, ...):\n", "\"Use 'cluster.params = list(n = your_value)' instead of 'nCluster'.\n", " The nCluster parameter will be removed in the next version.\"\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[1] \"enrichment_plots//enrichment/3/goo_up/treeplot 8 12\"\n", "[1] \"Saving to file enrichment_plots//enrichment/3/goo_up/treeplot.png\"\n", "[1] \"Saving to file enrichment_plots//enrichment/3/goo_up/treeplot.pdf\"\n", "[1] \"Saving to file enrichment_plots//enrichment/3/goo_up/treeplot.svg\"\n", "[1] \"enrichment_plots//enrichment/3/goo_up/emapplot 8 12\"\n", "[1] \"Saving to file enrichment_plots//enrichment/3/goo_up/emapplot.png\"\n", "[1] \"Saving to file enrichment_plots//enrichment/3/goo_up/emapplot.pdf\"\n", "[1] \"Saving to file enrichment_plots//enrichment/3/goo_up/emapplot.svg\"\n", "[1] \"3 goo_down\"\n", "[1] \"enrichment_plots//enrichment/3/goo_down/dotplot_30 6 12\"\n", "[1] \"Saving to file enrichment_plots//enrichment/3/goo_down/dotplot_30.png\"\n", "[1] \"Saving to file enrichment_plots//enrichment/3/goo_down/dotplot_30.pdf\"\n", "[1] \"Saving to file enrichment_plots//enrichment/3/goo_down/dotplot_30.svg\"\n", "[1] \"Saving to file enrichment_plots//enrichment/3/goo_down/dotplot_30.data\"\n", "[1] \"enrichment_plots//enrichment/3/goo_down/barplot_qvalue_30 6 12\"\n", "[1] \"Saving to file enrichment_plots//enrichment/3/goo_down/barplot_qvalue_30.png\"\n", "[1] \"Saving to file enrichment_plots//enrichment/3/goo_down/barplot_qvalue_30.pdf\"\n", "[1] \"Saving to file enrichment_plots//enrichment/3/goo_down/barplot_qvalue_30.svg\"\n", "[1] \"Saving to file enrichment_plots//enrichment/3/goo_down/barplot_qvalue_30.data\"\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "\u001b[1m\u001b[22mScale for \u001b[32msize\u001b[39m is already present.\n", "Adding another scale for \u001b[32msize\u001b[39m, which will replace the existing scale.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[1] \"enrichment_plots//enrichment/3/goo_down/cnetplot 12 12\"\n", "[1] \"Saving to file enrichment_plots//enrichment/3/goo_down/cnetplot.png\"\n", "[1] \"Saving to file enrichment_plots//enrichment/3/goo_down/cnetplot.pdf\"\n", "[1] \"Saving to file enrichment_plots//enrichment/3/goo_down/cnetplot.svg\"\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Warning message in treeplot.enrichResult(x, ...):\n", "\"Use 'cluster.params = list(n = your_value)' instead of 'nCluster'.\n", " The nCluster parameter will be removed in the next version.\"\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[1] \"enrichment_plots//enrichment/3/goo_down/treeplot 8 12\"\n", "[1] \"Saving to file enrichment_plots//enrichment/3/goo_down/treeplot.png\"\n", "[1] \"Saving to file enrichment_plots//enrichment/3/goo_down/treeplot.pdf\"\n", "[1] \"Saving to file enrichment_plots//enrichment/3/goo_down/treeplot.svg\"\n", "[1] \"enrichment_plots//enrichment/3/goo_down/emapplot 8 12\"\n", "[1] \"Saving to file enrichment_plots//enrichment/3/goo_down/emapplot.png\"\n", "[1] \"Saving to file enrichment_plots//enrichment/3/goo_down/emapplot.pdf\"\n", "[1] \"Saving to file enrichment_plots//enrichment/3/goo_down/emapplot.svg\"\n", "[1] \"4 rao\"\n", "[1] \"enrichment_plots//enrichment/4/rao/dotplot_30 6 12\"\n", "[1] \"Saving to file enrichment_plots//enrichment/4/rao/dotplot_30.png\"\n", "[1] \"Saving to file enrichment_plots//enrichment/4/rao/dotplot_30.pdf\"\n", "[1] \"Saving to file enrichment_plots//enrichment/4/rao/dotplot_30.svg\"\n", "[1] \"Saving to file enrichment_plots//enrichment/4/rao/dotplot_30.data\"\n", "[1] \"enrichment_plots//enrichment/4/rao/barplot_qvalue_30 6 12\"\n", "[1] \"Saving to file enrichment_plots//enrichment/4/rao/barplot_qvalue_30.png\"\n", "[1] \"Saving to file enrichment_plots//enrichment/4/rao/barplot_qvalue_30.pdf\"\n", "[1] \"Saving to file enrichment_plots//enrichment/4/rao/barplot_qvalue_30.svg\"\n", "[1] \"Saving to file enrichment_plots//enrichment/4/rao/barplot_qvalue_30.data\"\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "\u001b[1m\u001b[22mScale for \u001b[32msize\u001b[39m is already present.\n", "Adding another scale for \u001b[32msize\u001b[39m, which will replace the existing scale.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[1] \"enrichment_plots//enrichment/4/rao/cnetplot 12 12\"\n", "[1] \"Saving to file enrichment_plots//enrichment/4/rao/cnetplot.png\"\n", "[1] \"Saving to file enrichment_plots//enrichment/4/rao/cnetplot.pdf\"\n", "[1] \"Saving to file enrichment_plots//enrichment/4/rao/cnetplot.svg\"\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Warning message in treeplot.enrichResult(x, ...):\n", "\"Use 'cluster.params = list(n = your_value)' instead of 'nCluster'.\n", " The nCluster parameter will be removed in the next version.\"\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[1] \"enrichment_plots//enrichment/4/rao/treeplot 8 12\"\n", "[1] \"Saving to file enrichment_plots//enrichment/4/rao/treeplot.png\"\n", "[1] \"Saving to file enrichment_plots//enrichment/4/rao/treeplot.pdf\"\n", "[1] \"Saving to file enrichment_plots//enrichment/4/rao/treeplot.svg\"\n", "[1] \"enrichment_plots//enrichment/4/rao/emapplot 8 12\"\n", "[1] \"Saving to file enrichment_plots//enrichment/4/rao/emapplot.png\"\n", "[1] \"Saving to file enrichment_plots//enrichment/4/rao/emapplot.pdf\"\n", "[1] \"Saving to file enrichment_plots//enrichment/4/rao/emapplot.svg\"\n", "[1] \"4 rao_up\"\n", "[1] 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enrichment_plots//enrichment/4/rao_up/treeplot.svg\"\n", "[1] \"enrichment_plots//enrichment/4/rao_up/emapplot 8 12\"\n", "[1] \"Saving to file enrichment_plots//enrichment/4/rao_up/emapplot.png\"\n", "[1] \"Saving to file enrichment_plots//enrichment/4/rao_up/emapplot.pdf\"\n", "[1] \"Saving to file enrichment_plots//enrichment/4/rao_up/emapplot.svg\"\n", "[1] \"4 rao_down\"\n", "[1] \"enrichment_plots//enrichment/4/rao_down/dotplot_30 6 12\"\n", "[1] \"Saving to file enrichment_plots//enrichment/4/rao_down/dotplot_30.png\"\n", "[1] \"Saving to file enrichment_plots//enrichment/4/rao_down/dotplot_30.pdf\"\n", "[1] \"Saving to file enrichment_plots//enrichment/4/rao_down/dotplot_30.svg\"\n", "[1] \"Saving to file enrichment_plots//enrichment/4/rao_down/dotplot_30.data\"\n", "[1] \"enrichment_plots//enrichment/4/rao_down/barplot_qvalue_30 6 12\"\n", "[1] \"Saving to file enrichment_plots//enrichment/4/rao_down/barplot_qvalue_30.png\"\n", "[1] \"Saving to file enrichment_plots//enrichment/4/rao_down/barplot_qvalue_30.pdf\"\n", "[1] \"Saving to file enrichment_plots//enrichment/4/rao_down/barplot_qvalue_30.svg\"\n", "[1] \"Saving to file enrichment_plots//enrichment/4/rao_down/barplot_qvalue_30.data\"\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "\u001b[1m\u001b[22mScale for \u001b[32msize\u001b[39m is already present.\n", "Adding another scale for \u001b[32msize\u001b[39m, which will replace the existing scale.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[1] \"enrichment_plots//enrichment/4/rao_down/cnetplot 12 12\"\n", "[1] \"Saving to file enrichment_plots//enrichment/4/rao_down/cnetplot.png\"\n", "[1] \"Saving to file enrichment_plots//enrichment/4/rao_down/cnetplot.pdf\"\n", "[1] \"Saving to file enrichment_plots//enrichment/4/rao_down/cnetplot.svg\"\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Warning message in treeplot.enrichResult(x, ...):\n", "\"Use 'cluster.params = list(n = your_value)' instead of 'nCluster'.\n", " The nCluster parameter will be removed in the next version.\"\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[1] \"enrichment_plots//enrichment/4/rao_down/treeplot 8 12\"\n", "[1] \"Saving to file enrichment_plots//enrichment/4/rao_down/treeplot.png\"\n", "[1] \"Saving to file enrichment_plots//enrichment/4/rao_down/treeplot.pdf\"\n", "[1] \"Saving to file enrichment_plots//enrichment/4/rao_down/treeplot.svg\"\n", "[1] \"enrichment_plots//enrichment/4/rao_down/emapplot 8 12\"\n", "[1] \"Saving to file enrichment_plots//enrichment/4/rao_down/emapplot.png\"\n", "[1] \"Saving to file enrichment_plots//enrichment/4/rao_down/emapplot.pdf\"\n", "[1] \"Saving to file enrichment_plots//enrichment/4/rao_down/emapplot.svg\"\n", "[1] \"4 keggo\"\n", "[1] \"enrichment_plots//enrichment/4/keggo/dotplot_30 6 12\"\n", "[1] \"Saving to file enrichment_plots//enrichment/4/keggo/dotplot_30.png\"\n", "[1] \"Saving to file enrichment_plots//enrichment/4/keggo/dotplot_30.pdf\"\n", "[1] \"Saving to file enrichment_plots//enrichment/4/keggo/dotplot_30.svg\"\n", "[1] \"Saving to file enrichment_plots//enrichment/4/keggo/dotplot_30.data\"\n", "[1] \"enrichment_plots//enrichment/4/keggo/barplot_qvalue_30 6 12\"\n", "[1] \"Saving to file enrichment_plots//enrichment/4/keggo/barplot_qvalue_30.png\"\n", "[1] \"Saving to file enrichment_plots//enrichment/4/keggo/barplot_qvalue_30.pdf\"\n", "[1] \"Saving to file enrichment_plots//enrichment/4/keggo/barplot_qvalue_30.svg\"\n", "[1] \"Saving to file enrichment_plots//enrichment/4/keggo/barplot_qvalue_30.data\"\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "\u001b[1m\u001b[22mScale for \u001b[32msize\u001b[39m is already present.\n", "Adding another scale for \u001b[32msize\u001b[39m, which will replace the existing scale.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[1] \"enrichment_plots//enrichment/4/keggo/cnetplot 12 12\"\n", "[1] \"Saving to file enrichment_plots//enrichment/4/keggo/cnetplot.png\"\n", "[1] \"Saving to file enrichment_plots//enrichment/4/keggo/cnetplot.pdf\"\n", "[1] \"Saving to file enrichment_plots//enrichment/4/keggo/cnetplot.svg\"\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Warning message in treeplot.enrichResult(x, ...):\n", "\"Use 'cluster.params = list(n = your_value)' instead of 'nCluster'.\n", " The nCluster parameter will be removed in the next version.\"\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[1] \"enrichment_plots//enrichment/4/keggo/treeplot 8 12\"\n", "[1] \"Saving to file enrichment_plots//enrichment/4/keggo/treeplot.png\"\n", "[1] \"Saving to file enrichment_plots//enrichment/4/keggo/treeplot.pdf\"\n", "[1] \"Saving to file enrichment_plots//enrichment/4/keggo/treeplot.svg\"\n", "[1] \"enrichment_plots//enrichment/4/keggo/emapplot 8 12\"\n", "[1] \"Saving to file enrichment_plots//enrichment/4/keggo/emapplot.png\"\n", "[1] \"Saving to file enrichment_plots//enrichment/4/keggo/emapplot.pdf\"\n", "[1] \"Saving to file enrichment_plots//enrichment/4/keggo/emapplot.svg\"\n", "[1] \"4 keggo_up\"\n", "[1] \"enrichment_plots//enrichment/4/keggo_up/dotplot_30 6 12\"\n", "[1] \"Saving to file enrichment_plots//enrichment/4/keggo_up/dotplot_30.png\"\n", "[1] \"Saving to file enrichment_plots//enrichment/4/keggo_up/dotplot_30.pdf\"\n", "[1] \"Saving to file enrichment_plots//enrichment/4/keggo_up/dotplot_30.svg\"\n", "[1] \"Saving to file enrichment_plots//enrichment/4/keggo_up/dotplot_30.data\"\n", "[1] \"enrichment_plots//enrichment/4/keggo_up/barplot_qvalue_30 6 12\"\n", "[1] \"Saving to file enrichment_plots//enrichment/4/keggo_up/barplot_qvalue_30.png\"\n", "[1] \"Saving to file enrichment_plots//enrichment/4/keggo_up/barplot_qvalue_30.pdf\"\n", "[1] \"Saving to file enrichment_plots//enrichment/4/keggo_up/barplot_qvalue_30.svg\"\n", "[1] \"Saving to file enrichment_plots//enrichment/4/keggo_up/barplot_qvalue_30.data\"\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "\u001b[1m\u001b[22mScale for \u001b[32msize\u001b[39m is already present.\n", "Adding another scale for \u001b[32msize\u001b[39m, which will replace the existing scale.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[1] \"enrichment_plots//enrichment/4/keggo_up/cnetplot 12 12\"\n", "[1] \"Saving to file enrichment_plots//enrichment/4/keggo_up/cnetplot.png\"\n", "[1] \"Saving to file enrichment_plots//enrichment/4/keggo_up/cnetplot.pdf\"\n", "[1] \"Saving to file enrichment_plots//enrichment/4/keggo_up/cnetplot.svg\"\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Warning message in treeplot.enrichResult(x, ...):\n", "\"Use 'cluster.params = list(n = your_value)' instead of 'nCluster'.\n", " The nCluster parameter will be removed in the next version.\"\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[1] 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enrichment_plots//enrichment/4/keggo_down/dotplot_30.data\"\n", "[1] \"enrichment_plots//enrichment/4/keggo_down/barplot_qvalue_30 6 12\"\n", "[1] \"Saving to file enrichment_plots//enrichment/4/keggo_down/barplot_qvalue_30.png\"\n", "[1] \"Saving to file enrichment_plots//enrichment/4/keggo_down/barplot_qvalue_30.pdf\"\n", "[1] \"Saving to file enrichment_plots//enrichment/4/keggo_down/barplot_qvalue_30.svg\"\n", "[1] \"Saving to file enrichment_plots//enrichment/4/keggo_down/barplot_qvalue_30.data\"\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "\u001b[1m\u001b[22mScale for \u001b[32msize\u001b[39m is already present.\n", "Adding another scale for \u001b[32msize\u001b[39m, which will replace the existing scale.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[1] \"enrichment_plots//enrichment/4/keggo_down/cnetplot 12 12\"\n", "[1] \"Saving to file enrichment_plots//enrichment/4/keggo_down/cnetplot.png\"\n", "[1] \"Saving to file enrichment_plots//enrichment/4/keggo_down/cnetplot.pdf\"\n", "[1] \"Saving to file enrichment_plots//enrichment/4/keggo_down/cnetplot.svg\"\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Warning message in treeplot.enrichResult(x, ...):\n", "\"Use 'cluster.params = list(n = your_value)' instead of 'nCluster'.\n", " The nCluster parameter will be removed in the next version.\"\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[1] \"enrichment_plots//enrichment/4/keggo_down/treeplot 8 12\"\n", "[1] \"Saving to file enrichment_plots//enrichment/4/keggo_down/treeplot.png\"\n", "[1] \"Saving to file enrichment_plots//enrichment/4/keggo_down/treeplot.pdf\"\n", "[1] \"Saving to file enrichment_plots//enrichment/4/keggo_down/treeplot.svg\"\n", "[1] \"enrichment_plots//enrichment/4/keggo_down/emapplot 8 12\"\n", "[1] \"Saving to file enrichment_plots//enrichment/4/keggo_down/emapplot.png\"\n", "[1] \"Saving to file enrichment_plots//enrichment/4/keggo_down/emapplot.pdf\"\n", "[1] \"Saving to file enrichment_plots//enrichment/4/keggo_down/emapplot.svg\"\n", "[1] \"4 mkeggo\"\n", "[1] \"4 mkeggo_up\"\n", "[1] \"4 mkeggo_down\"\n", "[1] \"4 goo\"\n", "[1] \"enrichment_plots//enrichment/4/goo/dotplot_30 6 12\"\n", "[1] \"Saving to file enrichment_plots//enrichment/4/goo/dotplot_30.png\"\n", "[1] \"Saving to file enrichment_plots//enrichment/4/goo/dotplot_30.pdf\"\n", "[1] \"Saving to file enrichment_plots//enrichment/4/goo/dotplot_30.svg\"\n", "[1] \"Saving to file enrichment_plots//enrichment/4/goo/dotplot_30.data\"\n", "[1] \"enrichment_plots//enrichment/4/goo/barplot_qvalue_30 6 12\"\n", "[1] \"Saving to file enrichment_plots//enrichment/4/goo/barplot_qvalue_30.png\"\n", "[1] \"Saving to file enrichment_plots//enrichment/4/goo/barplot_qvalue_30.pdf\"\n", "[1] \"Saving to file enrichment_plots//enrichment/4/goo/barplot_qvalue_30.svg\"\n", "[1] \"Saving to file enrichment_plots//enrichment/4/goo/barplot_qvalue_30.data\"\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "\u001b[1m\u001b[22mScale for \u001b[32msize\u001b[39m is already present.\n", "Adding another scale for \u001b[32msize\u001b[39m, which will replace the existing scale.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[1] \"enrichment_plots//enrichment/4/goo/cnetplot 12 12\"\n", "[1] \"Saving to file enrichment_plots//enrichment/4/goo/cnetplot.png\"\n", "[1] \"Saving to file enrichment_plots//enrichment/4/goo/cnetplot.pdf\"\n", "[1] \"Saving to file enrichment_plots//enrichment/4/goo/cnetplot.svg\"\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Warning message in treeplot.enrichResult(x, ...):\n", "\"Use 'cluster.params = list(n = your_value)' instead of 'nCluster'.\n", " The nCluster parameter will be removed in the next version.\"\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[1] 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enrichment_plots//enrichment/4/goo_down/cnetplot.pdf\"\n", "[1] \"Saving to file enrichment_plots//enrichment/4/goo_down/cnetplot.svg\"\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Warning message in treeplot.enrichResult(x, ...):\n", "\"Use 'cluster.params = list(n = your_value)' instead of 'nCluster'.\n", " The nCluster parameter will be removed in the next version.\"\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[1] \"enrichment_plots//enrichment/4/goo_down/treeplot 8 12\"\n", "[1] \"Saving to file enrichment_plots//enrichment/4/goo_down/treeplot.png\"\n", "[1] \"Saving to file enrichment_plots//enrichment/4/goo_down/treeplot.pdf\"\n", "[1] \"Saving to file enrichment_plots//enrichment/4/goo_down/treeplot.svg\"\n", "[1] \"enrichment_plots//enrichment/4/goo_down/emapplot 8 12\"\n", "[1] \"Saving to file enrichment_plots//enrichment/4/goo_down/emapplot.png\"\n", "[1] \"Saving to file enrichment_plots//enrichment/4/goo_down/emapplot.pdf\"\n", "[1] 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enrichment_plots//enrichment/5/rao_down/barplot_qvalue_30.pdf\"\n", "[1] \"Saving to file enrichment_plots//enrichment/5/rao_down/barplot_qvalue_30.svg\"\n", "[1] \"Saving to file enrichment_plots//enrichment/5/rao_down/barplot_qvalue_30.data\"\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "\u001b[1m\u001b[22mScale for \u001b[32msize\u001b[39m is already present.\n", "Adding another scale for \u001b[32msize\u001b[39m, which will replace the existing scale.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[1] \"enrichment_plots//enrichment/5/rao_down/cnetplot 12 12\"\n", "[1] \"Saving to file enrichment_plots//enrichment/5/rao_down/cnetplot.png\"\n", "[1] \"Saving to file enrichment_plots//enrichment/5/rao_down/cnetplot.pdf\"\n", "[1] \"Saving to file enrichment_plots//enrichment/5/rao_down/cnetplot.svg\"\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Warning message in treeplot.enrichResult(x, ...):\n", "\"Use 'cluster.params = list(n = 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enrichment_plots//enrichment/5/keggo_down/barplot_qvalue_30.data\"\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "\u001b[1m\u001b[22mScale for \u001b[32msize\u001b[39m is already present.\n", "Adding another scale for \u001b[32msize\u001b[39m, which will replace the existing scale.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[1] \"enrichment_plots//enrichment/5/keggo_down/cnetplot 12 12\"\n", "[1] \"Saving to file enrichment_plots//enrichment/5/keggo_down/cnetplot.png\"\n", "[1] \"Saving to file enrichment_plots//enrichment/5/keggo_down/cnetplot.pdf\"\n", "[1] \"Saving to file enrichment_plots//enrichment/5/keggo_down/cnetplot.svg\"\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Warning message in treeplot.enrichResult(x, ...):\n", "\"Use 'cluster.params = list(n = your_value)' instead of 'nCluster'.\n", " The nCluster parameter will be removed in the next version.\"\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ 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enrichment_plots//enrichment/5/goo/dotplot_30.svg\"\n", "[1] \"Saving to file enrichment_plots//enrichment/5/goo/dotplot_30.data\"\n", "[1] \"enrichment_plots//enrichment/5/goo/barplot_qvalue_30 6 12\"\n", "[1] \"Saving to file enrichment_plots//enrichment/5/goo/barplot_qvalue_30.png\"\n", "[1] \"Saving to file enrichment_plots//enrichment/5/goo/barplot_qvalue_30.pdf\"\n", "[1] \"Saving to file enrichment_plots//enrichment/5/goo/barplot_qvalue_30.svg\"\n", "[1] \"Saving to file enrichment_plots//enrichment/5/goo/barplot_qvalue_30.data\"\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "\u001b[1m\u001b[22mScale for \u001b[32msize\u001b[39m is already present.\n", "Adding another scale for \u001b[32msize\u001b[39m, which will replace the existing scale.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[1] \"enrichment_plots//enrichment/5/goo/cnetplot 12 12\"\n", "[1] \"Saving to file enrichment_plots//enrichment/5/goo/cnetplot.png\"\n", "[1] \"Saving to file enrichment_plots//enrichment/5/goo/cnetplot.pdf\"\n", "[1] \"Saving to file enrichment_plots//enrichment/5/goo/cnetplot.svg\"\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Warning message in treeplot.enrichResult(x, ...):\n", "\"Use 'cluster.params = list(n = your_value)' instead of 'nCluster'.\n", " The nCluster parameter will be removed in the next version.\"\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[1] \"enrichment_plots//enrichment/5/goo/treeplot 8 12\"\n", "[1] \"Saving to file enrichment_plots//enrichment/5/goo/treeplot.png\"\n", "[1] \"Saving to file enrichment_plots//enrichment/5/goo/treeplot.pdf\"\n", "[1] \"Saving to file enrichment_plots//enrichment/5/goo/treeplot.svg\"\n", "[1] \"enrichment_plots//enrichment/5/goo/emapplot 8 12\"\n", "[1] \"Saving to file enrichment_plots//enrichment/5/goo/emapplot.png\"\n", "[1] \"Saving to file enrichment_plots//enrichment/5/goo/emapplot.pdf\"\n", "[1] \"Saving to file enrichment_plots//enrichment/5/goo/emapplot.svg\"\n", "[1] \"5 goo_up\"\n", "[1] \"5 goo_down\"\n", "[1] \"enrichment_plots//enrichment/5/goo_down/dotplot_30 6 12\"\n", "[1] \"Saving to file enrichment_plots//enrichment/5/goo_down/dotplot_30.png\"\n", "[1] \"Saving to file enrichment_plots//enrichment/5/goo_down/dotplot_30.pdf\"\n", "[1] \"Saving to file enrichment_plots//enrichment/5/goo_down/dotplot_30.svg\"\n", "[1] \"Saving to file enrichment_plots//enrichment/5/goo_down/dotplot_30.data\"\n", "[1] \"enrichment_plots//enrichment/5/goo_down/barplot_qvalue_30 6 12\"\n", "[1] \"Saving to file enrichment_plots//enrichment/5/goo_down/barplot_qvalue_30.png\"\n", "[1] \"Saving to file enrichment_plots//enrichment/5/goo_down/barplot_qvalue_30.pdf\"\n", "[1] \"Saving to file enrichment_plots//enrichment/5/goo_down/barplot_qvalue_30.svg\"\n", "[1] \"Saving to file enrichment_plots//enrichment/5/goo_down/barplot_qvalue_30.data\"\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ 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enrichment_plots//enrichment/5/goo_down/treeplot.png\"\n", "[1] \"Saving to file enrichment_plots//enrichment/5/goo_down/treeplot.pdf\"\n", "[1] \"Saving to file enrichment_plots//enrichment/5/goo_down/treeplot.svg\"\n", "[1] \"enrichment_plots//enrichment/5/goo_down/emapplot 8 12\"\n", "[1] \"Saving to file enrichment_plots//enrichment/5/goo_down/emapplot.png\"\n", "[1] \"Saving to file enrichment_plots//enrichment/5/goo_down/emapplot.pdf\"\n", "[1] \"Saving to file enrichment_plots//enrichment/5/goo_down/emapplot.svg\"\n", "[1] \"6 rao\"\n", "[1] \"6 rao_up\"\n", "[1] \"6 rao_down\"\n", "[1] \"6 keggo\"\n", "[1] \"6 keggo_up\"\n", "[1] \"6 keggo_down\"\n", "[1] \"6 mkeggo\"\n", "[1] \"6 mkeggo_up\"\n", "[1] \"6 mkeggo_down\"\n", "[1] \"6 goo\"\n", "[1] \"6 goo_up\"\n", "[1] \"6 goo_down\"\n", "[1] \"7 rao\"\n", "[1] \"enrichment_plots//enrichment/7/rao/dotplot_30 6 12\"\n", "[1] \"Saving to file enrichment_plots//enrichment/7/rao/dotplot_30.png\"\n", "[1] \"Saving to file 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file enrichment_plots//enrichment/7/rao/cnetplot.png\"\n", "[1] \"Saving to file enrichment_plots//enrichment/7/rao/cnetplot.pdf\"\n", "[1] \"Saving to file enrichment_plots//enrichment/7/rao/cnetplot.svg\"\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Warning message in treeplot.enrichResult(x, ...):\n", "\"Use 'cluster.params = list(n = your_value)' instead of 'nCluster'.\n", " The nCluster parameter will be removed in the next version.\"\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[1] \"enrichment_plots//enrichment/7/rao/treeplot 8 12\"\n", "[1] \"Saving to file enrichment_plots//enrichment/7/rao/treeplot.png\"\n", "[1] \"Saving to file enrichment_plots//enrichment/7/rao/treeplot.pdf\"\n", "[1] \"Saving to file enrichment_plots//enrichment/7/rao/treeplot.svg\"\n", "[1] \"enrichment_plots//enrichment/7/rao/emapplot 8 12\"\n", "[1] \"Saving to file enrichment_plots//enrichment/7/rao/emapplot.png\"\n", "[1] \"Saving to file enrichment_plots//enrichment/7/rao/emapplot.pdf\"\n", "[1] \"Saving to file enrichment_plots//enrichment/7/rao/emapplot.svg\"\n", "[1] \"7 rao_up\"\n", "[1] \"7 rao_down\"\n", "[1] \"enrichment_plots//enrichment/7/rao_down/dotplot_30 6 12\"\n", "[1] \"Saving to file enrichment_plots//enrichment/7/rao_down/dotplot_30.png\"\n", "[1] \"Saving to file enrichment_plots//enrichment/7/rao_down/dotplot_30.pdf\"\n", "[1] \"Saving to file enrichment_plots//enrichment/7/rao_down/dotplot_30.svg\"\n", "[1] \"Saving to file enrichment_plots//enrichment/7/rao_down/dotplot_30.data\"\n", "[1] \"enrichment_plots//enrichment/7/rao_down/barplot_qvalue_30 6 12\"\n", "[1] \"Saving to file enrichment_plots//enrichment/7/rao_down/barplot_qvalue_30.png\"\n", "[1] \"Saving to file enrichment_plots//enrichment/7/rao_down/barplot_qvalue_30.pdf\"\n", "[1] \"Saving to file enrichment_plots//enrichment/7/rao_down/barplot_qvalue_30.svg\"\n", "[1] \"Saving to file enrichment_plots//enrichment/7/rao_down/barplot_qvalue_30.data\"\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "\u001b[1m\u001b[22mScale for \u001b[32msize\u001b[39m is already present.\n", "Adding another scale for \u001b[32msize\u001b[39m, which will replace the existing scale.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[1] \"enrichment_plots//enrichment/7/rao_down/cnetplot 12 12\"\n", "[1] \"Saving to file enrichment_plots//enrichment/7/rao_down/cnetplot.png\"\n", "[1] \"Saving to file enrichment_plots//enrichment/7/rao_down/cnetplot.pdf\"\n", "[1] \"Saving to file enrichment_plots//enrichment/7/rao_down/cnetplot.svg\"\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Warning message in treeplot.enrichResult(x, ...):\n", "\"Use 'cluster.params = list(n = your_value)' instead of 'nCluster'.\n", " The nCluster parameter will be removed in the next version.\"\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[1] 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enrichment_plots//enrichment/7/keggo/dotplot_30.data\"\n", "[1] \"enrichment_plots//enrichment/7/keggo/barplot_qvalue_30 6 12\"\n", "[1] \"Saving to file enrichment_plots//enrichment/7/keggo/barplot_qvalue_30.png\"\n", "[1] \"Saving to file enrichment_plots//enrichment/7/keggo/barplot_qvalue_30.pdf\"\n", "[1] \"Saving to file enrichment_plots//enrichment/7/keggo/barplot_qvalue_30.svg\"\n", "[1] \"Saving to file enrichment_plots//enrichment/7/keggo/barplot_qvalue_30.data\"\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "\u001b[1m\u001b[22mScale for \u001b[32msize\u001b[39m is already present.\n", "Adding another scale for \u001b[32msize\u001b[39m, which will replace the existing scale.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[1] \"enrichment_plots//enrichment/7/keggo/cnetplot 12 12\"\n", "[1] \"Saving to file enrichment_plots//enrichment/7/keggo/cnetplot.png\"\n", "[1] \"Saving to file enrichment_plots//enrichment/7/keggo/cnetplot.pdf\"\n", "[1] 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keggo_up\"\n", "[1] \"7 keggo_down\"\n", "[1] \"enrichment_plots//enrichment/7/keggo_down/dotplot_30 6 12\"\n", "[1] \"Saving to file enrichment_plots//enrichment/7/keggo_down/dotplot_30.png\"\n", "[1] \"Saving to file enrichment_plots//enrichment/7/keggo_down/dotplot_30.pdf\"\n", "[1] \"Saving to file enrichment_plots//enrichment/7/keggo_down/dotplot_30.svg\"\n", "[1] \"Saving to file enrichment_plots//enrichment/7/keggo_down/dotplot_30.data\"\n", "[1] \"enrichment_plots//enrichment/7/keggo_down/barplot_qvalue_30 6 12\"\n", "[1] \"Saving to file enrichment_plots//enrichment/7/keggo_down/barplot_qvalue_30.png\"\n", "[1] \"Saving to file enrichment_plots//enrichment/7/keggo_down/barplot_qvalue_30.pdf\"\n", "[1] \"Saving to file enrichment_plots//enrichment/7/keggo_down/barplot_qvalue_30.svg\"\n", "[1] \"Saving to file enrichment_plots//enrichment/7/keggo_down/barplot_qvalue_30.data\"\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "\u001b[1m\u001b[22mScale for 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scale.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[1] \"enrichment_plots//enrichment/7/goo_down/cnetplot 12 12\"\n", "[1] \"Saving to file enrichment_plots//enrichment/7/goo_down/cnetplot.png\"\n", "[1] \"Saving to file enrichment_plots//enrichment/7/goo_down/cnetplot.pdf\"\n", "[1] \"Saving to file enrichment_plots//enrichment/7/goo_down/cnetplot.svg\"\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Warning message in treeplot.enrichResult(x, ...):\n", "\"Use 'cluster.params = list(n = your_value)' instead of 'nCluster'.\n", " The nCluster parameter will be removed in the next version.\"\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[1] \"enrichment_plots//enrichment/7/goo_down/treeplot 8 12\"\n", "[1] \"Saving to file enrichment_plots//enrichment/7/goo_down/treeplot.png\"\n", "[1] \"Saving to file enrichment_plots//enrichment/7/goo_down/treeplot.pdf\"\n", "[1] \"Saving to file enrichment_plots//enrichment/7/goo_down/treeplot.svg\"\n", "[1] \"enrichment_plots//enrichment/7/goo_down/emapplot 8 12\"\n", "[1] \"Saving to file enrichment_plots//enrichment/7/goo_down/emapplot.png\"\n", "[1] \"Saving to file enrichment_plots//enrichment/7/goo_down/emapplot.pdf\"\n", "[1] \"Saving to file enrichment_plots//enrichment/7/goo_down/emapplot.svg\"\n", "[1] \"8 rao\"\n", "[1] \"enrichment_plots//enrichment/8/rao/dotplot_30 6 12\"\n", "[1] \"Saving to file enrichment_plots//enrichment/8/rao/dotplot_30.png\"\n", "[1] \"Saving to file enrichment_plots//enrichment/8/rao/dotplot_30.pdf\"\n", "[1] \"Saving to file enrichment_plots//enrichment/8/rao/dotplot_30.svg\"\n", "[1] \"Saving to file enrichment_plots//enrichment/8/rao/dotplot_30.data\"\n", "[1] \"enrichment_plots//enrichment/8/rao/barplot_qvalue_30 6 12\"\n", "[1] \"Saving to file enrichment_plots//enrichment/8/rao/barplot_qvalue_30.png\"\n", "[1] \"Saving to file enrichment_plots//enrichment/8/rao/barplot_qvalue_30.pdf\"\n", "[1] \"Saving to file enrichment_plots//enrichment/8/rao/barplot_qvalue_30.svg\"\n", "[1] \"Saving to file enrichment_plots//enrichment/8/rao/barplot_qvalue_30.data\"\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "\u001b[1m\u001b[22mScale for \u001b[32msize\u001b[39m is already present.\n", "Adding another scale for \u001b[32msize\u001b[39m, which will replace the existing scale.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[1] \"enrichment_plots//enrichment/8/rao/cnetplot 12 12\"\n", "[1] \"Saving to file enrichment_plots//enrichment/8/rao/cnetplot.png\"\n", "[1] \"Saving to file enrichment_plots//enrichment/8/rao/cnetplot.pdf\"\n", "[1] \"Saving to file enrichment_plots//enrichment/8/rao/cnetplot.svg\"\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Warning message in treeplot.enrichResult(x, ...):\n", "\"Use 'cluster.params = list(n = your_value)' instead of 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keggo_up\"\n", "[1] \"enrichment_plots//enrichment/8/keggo_up/dotplot_30 6 12\"\n", "[1] \"Saving to file enrichment_plots//enrichment/8/keggo_up/dotplot_30.png\"\n", "[1] \"Saving to file enrichment_plots//enrichment/8/keggo_up/dotplot_30.pdf\"\n", "[1] \"Saving to file enrichment_plots//enrichment/8/keggo_up/dotplot_30.svg\"\n", "[1] \"Saving to file enrichment_plots//enrichment/8/keggo_up/dotplot_30.data\"\n", "[1] \"enrichment_plots//enrichment/8/keggo_up/barplot_qvalue_30 6 12\"\n", "[1] \"Saving to file enrichment_plots//enrichment/8/keggo_up/barplot_qvalue_30.png\"\n", "[1] \"Saving to file enrichment_plots//enrichment/8/keggo_up/barplot_qvalue_30.pdf\"\n", "[1] \"Saving to file enrichment_plots//enrichment/8/keggo_up/barplot_qvalue_30.svg\"\n", "[1] \"Saving to file enrichment_plots//enrichment/8/keggo_up/barplot_qvalue_30.data\"\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "\u001b[1m\u001b[22mScale for \u001b[32msize\u001b[39m is already present.\n", "Adding 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enrichment_plots//enrichment/12/rao_up/barplot_qvalue_30.svg\"\n", "[1] \"Saving to file enrichment_plots//enrichment/12/rao_up/barplot_qvalue_30.data\"\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "\u001b[1m\u001b[22mScale for \u001b[32msize\u001b[39m is already present.\n", "Adding another scale for \u001b[32msize\u001b[39m, which will replace the existing scale.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[1] \"enrichment_plots//enrichment/12/rao_up/cnetplot 12 12\"\n", "[1] \"Saving to file enrichment_plots//enrichment/12/rao_up/cnetplot.png\"\n", "[1] \"Saving to file enrichment_plots//enrichment/12/rao_up/cnetplot.pdf\"\n", "[1] \"Saving to file enrichment_plots//enrichment/12/rao_up/cnetplot.svg\"\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Warning message in treeplot.enrichResult(x, ...):\n", "\"Use 'cluster.params = list(n = your_value)' instead of 'nCluster'.\n", " The nCluster parameter will be removed in the next 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"text": [ "[1] \"enrichment_plots//enrichment/12/goo/cnetplot 12 12\"\n", "[1] \"Saving to file enrichment_plots//enrichment/12/goo/cnetplot.png\"\n", "[1] \"Saving to file enrichment_plots//enrichment/12/goo/cnetplot.pdf\"\n", "[1] \"Saving to file enrichment_plots//enrichment/12/goo/cnetplot.svg\"\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Warning message in treeplot.enrichResult(x, ...):\n", "\"Use 'cluster.params = list(n = your_value)' instead of 'nCluster'.\n", " The nCluster parameter will be removed in the next version.\"\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[1] \"enrichment_plots//enrichment/12/goo/treeplot 8 12\"\n", "[1] \"Saving to file enrichment_plots//enrichment/12/goo/treeplot.png\"\n", "[1] \"Saving to file enrichment_plots//enrichment/12/goo/treeplot.pdf\"\n", "[1] \"Saving to file enrichment_plots//enrichment/12/goo/treeplot.svg\"\n", "[1] \"enrichment_plots//enrichment/12/goo/emapplot 8 12\"\n", "[1] \"Saving to file 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enrichment_plots//enrichment/12/goo_up/barplot_qvalue_30.data\"\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "\u001b[1m\u001b[22mScale for \u001b[32msize\u001b[39m is already present.\n", "Adding another scale for \u001b[32msize\u001b[39m, which will replace the existing scale.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[1] \"enrichment_plots//enrichment/12/goo_up/cnetplot 12 12\"\n", "[1] \"Saving to file enrichment_plots//enrichment/12/goo_up/cnetplot.png\"\n", "[1] \"Saving to file enrichment_plots//enrichment/12/goo_up/cnetplot.pdf\"\n", "[1] \"Saving to file enrichment_plots//enrichment/12/goo_up/cnetplot.svg\"\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Warning message in treeplot.enrichResult(x, ...):\n", "\"Use 'cluster.params = list(n = your_value)' instead of 'nCluster'.\n", " The nCluster parameter will be removed in the next version.\"\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[1] \"enrichment_plots//enrichment/12/goo_up/treeplot 8 12\"\n", "[1] \"Saving to file enrichment_plots//enrichment/12/goo_up/treeplot.png\"\n", "[1] \"Saving to file enrichment_plots//enrichment/12/goo_up/treeplot.pdf\"\n", "[1] \"Saving to file enrichment_plots//enrichment/12/goo_up/treeplot.svg\"\n", "[1] \"enrichment_plots//enrichment/12/goo_up/emapplot 8 12\"\n", "[1] \"Saving to file enrichment_plots//enrichment/12/goo_up/emapplot.png\"\n", "[1] \"Saving to file enrichment_plots//enrichment/12/goo_up/emapplot.pdf\"\n", "[1] \"Saving to file enrichment_plots//enrichment/12/goo_up/emapplot.svg\"\n", "[1] \"12 goo_down\"\n", "[1] \"13 rao\"\n", "[1] \"13 rao_up\"\n", "[1] \"13 rao_down\"\n" ] } ], "source": [ "\n", "makeEnrichmentPlots( gseResults, outfolder = \"enrichment_plots/\" )\n", "\n" ] }, { "cell_type": "markdown", "id": "61fcf49d", "metadata": {}, "source": [ "\n", "\n", "\n", "\n", "\n", "\n", "\n" ] } ], "metadata": { "jupytext": { "cell_metadata_filter": "fig.width,fig.height,-all", "main_language": "R", "notebook_metadata_filter": "-all" }, "kernelspec": { "display_name": "R", "language": "R", "name": "ir" }, "language_info": { "codemirror_mode": "r", "file_extension": ".r", "mimetype": "text/x-r-source", "name": "R", "pygments_lexer": "r", "version": "4.3.1" } }, "nbformat": 4, "nbformat_minor": 5 }