Configuration Settings for the Workflow
species | tissue | metadata | genesets | genetype | min_umi_per_cell | max_mt_percent | min_genes_per_cell | min_cell | scr_th | seu_nrmlz_method | seu_scale_factor | seu_n_hvg | seu_n_dim | seu_k_param | seu_cluster_res | harmony | tsne | spr_n_dim | mrk_logfc | mrk_min_pct | mrk_only_pos | mrk_test | mrk_top_n | adt | trajectory | traj_var_gene | traj_top_n | pipe_version |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
hs | lung | cell_based | none | none | 750 | 10 | 250 | 3 | 0 | LogNormalize | 1e+06 | 2000 | 30 | 20 | 0.7 | patient_sample | FALSE | 30 | 0.25 | 0.5 | TRUE | wilcox | 25 | FALSE | none | 1000 | 50 | 1.0.0 |
sample | sample_id | tissue | disease | patient | patient_sample | magnetic.selection_celltypes | compartment_celltypes |
---|---|---|---|---|---|---|---|
all_samples | S1 | lung | normal | 2,1,3 | distal 2,medial 2,distal 1a,proximal 3,distal 3 | epithelial,immune and endothelial,stromal | Endothelial,Immune,Stromal,Epithelial |
sample_id | pre_qc_gene | pre_qc_cell | post_qc_gene | post_qc_cell |
---|---|---|---|---|
S1 | 22,005 | 60,993 | 20,814 | 60,993 |
Vales are post-QC.
sample_id | min | 0% | 25% | 50% | 75% | 100% | max |
---|---|---|---|---|---|---|---|
S1 | 1,001 | 1,001 | 2,380 | 4,097 | 11,081 | 135,621 | 135,621 |
Vales are post-QC.
sample_id | min | 0% | 25% | 50% | 75% | 100% | max |
---|---|---|---|---|---|---|---|
S1 | 501 | 501 | 1,070 | 1,552 | 2,712 | 9,594 | 9,594 |
Vales are post-QC.
sample_id | min | 0% | 25% | 50% | 75% | 100% | max |
---|---|---|---|---|---|---|---|
S1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
Thresholds, represented by dashed lines, were implemented to filter the data and only retain cells of high quality.
Number of barcodes shared between pairs of samples post-QC.
S1 | |
---|---|
S1 | 60993 |
Fraction (%) of barcodes shared between pairs of samples post-QC.
S1 | |
---|---|
S1 | 1 |
diamonds and diamonds refer to before and after QC, respectively.
The error bars represent the standard deviation of the number of UMIs and genes across cells per sample.
min | 0% | 25% | 50% | 75% | 100% | max | |
---|---|---|---|---|---|---|---|
UMI per cell | 1,001 | 1,001 | 2,380 | 4,097 | 11,081 | 135,621 | 135,621 |
Gene per cell | 501 | 501 | 1,070 | 1,552 | 2,712 | 9,594 | 9,594 |
Mitochondrial (%) per cell | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
Note: Labels may have been removed if they overlap excessively.
Note: QC metrics based on kernel density estimation.
Note: Labels may have been removed if they overlap excessively.
Values at the top of each bar indicate the percentage of cells
This measurement is a proxy to batch effect artifacts. Values adjacent to each point indicate the number of cells.
Top 25 differentially expressed genes ( p_val_adj < 0.05 and pct.1 > 0.5 ) for each of the clusters
Note: Labels may have been removed if they overlap excessively.
Values at the top of each bar indicate the percentage of cells
Publication: no referrence
Data Availability: no referrence
This is the output of sessionInfo()
on the computing
system on which this document was compiled
## R version 4.3.2 (2023-10-31)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 20.04.6 LTS
##
## Matrix products: default
## BLAS: /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.9.0
## LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.9.0
##
## Random number generation:
## RNG: L'Ecuyer-CMRG
## Normal: Inversion
## Sample: Rejection
##
## locale:
## [1] C
##
## time zone: Etc/UTC
## tzcode source: system (glibc)
##
## attached base packages:
## [1] stats4 grid stats graphics grDevices
## [6] utils datasets methods base
##
## other attached packages:
## [1] edgeR_4.0.2 limma_3.58.1
## [3] slingshot_2.10.0 TrajectoryUtils_1.10.0
## [5] SingleCellExperiment_1.24.0 SummarizedExperiment_1.32.0
## [7] Biobase_2.62.0 GenomicRanges_1.54.1
## [9] GenomeInfoDb_1.38.1 IRanges_2.36.0
## [11] S4Vectors_0.40.2 BiocGenerics_0.48.1
## [13] MatrixGenerics_1.14.0 matrixStats_1.1.0
## [15] princurve_2.1.6 Nebulosa_1.12.0
## [17] patchwork_1.1.3 data.table_1.14.10
## [19] ComplexHeatmap_2.18.0 visNetwork_2.1.2
## [21] plotly_4.10.3 data.tree_1.1.0
## [23] DT_0.30 readxl_1.4.3
## [25] gtools_3.9.5 gplots_3.1.3
## [27] gridtext_0.1.5 igraph_1.5.1
## [29] sargent_1.0.1 SignacX_2.2.5
## [31] harmony_1.2.0 Rcpp_1.0.11
## [33] kableExtra_1.3.4 purrr_1.0.2
## [35] reticulate_1.34.0 RColorBrewer_1.1-3
## [37] cowplot_1.1.1 gridExtra_2.3
## [39] pheatmap_1.0.12 ggrepel_0.9.4
## [41] ggplot2_3.4.4 Seurat_5.0.1
## [43] SeuratObject_5.0.1 sp_2.1-2
## [45] dplyr_1.1.4 optparse_1.7.3
##
## loaded via a namespace (and not attached):
## [1] spatstat.sparse_3.0-3 bitops_1.0-7
## [3] httr_1.4.7 webshot_0.5.5
## [5] doParallel_1.0.17 tools_4.3.2
## [7] sctransform_0.4.1 utf8_1.2.4
## [9] R6_2.5.1 mgcv_1.9-0
## [11] lazyeval_0.2.2 uwot_0.1.16
## [13] ggdist_3.3.1 GetoptLong_1.0.5
## [15] withr_2.5.2 progressr_0.14.0
## [17] cli_3.6.1 spatstat.explore_3.2-5
## [19] fastDummies_1.7.3 labeling_0.4.3
## [21] sass_0.4.8 mvtnorm_1.2-4
## [23] spatstat.data_3.0-3 proxy_0.4-27
## [25] ggridges_0.5.4 pbapply_1.7-2
## [27] commonmark_1.9.0 systemfonts_1.0.5
## [29] R.utils_2.12.3 svglite_2.1.2
## [31] parallelly_1.36.0 rstudioapi_0.15.0
## [33] generics_0.1.3 shape_1.4.6
## [35] crosstalk_1.2.1 ica_1.0-3
## [37] spatstat.random_3.2-2 distributional_0.3.2
## [39] Matrix_1.6-4 fansi_1.0.6
## [41] DescTools_0.99.52 abind_1.4-5
## [43] R.methodsS3_1.8.2 lifecycle_1.0.4
## [45] yaml_2.3.7 SparseArray_1.2.2
## [47] Rtsne_0.17 promises_1.2.1
## [49] crayon_1.5.2 miniUI_0.1.1.1
## [51] lattice_0.22-5 pillar_1.9.0
## [53] knitr_1.45 rjson_0.2.21
## [55] boot_1.3-28.1 gld_2.6.6
## [57] future.apply_1.11.0 codetools_0.2-19
## [59] leiden_0.4.3.1 glue_1.6.2
## [61] vctrs_0.6.5 png_0.1-8
## [63] spam_2.10-0 neuralnet_1.44.2
## [65] cellranger_1.1.0 gtable_0.3.4
## [67] cachem_1.0.8 ks_1.14.1
## [69] xfun_0.41 S4Arrays_1.2.0
## [71] mime_0.12 pracma_2.4.4
## [73] survival_3.5-7 pbmcapply_1.5.1
## [75] iterators_1.0.14 statmod_1.5.0
## [77] ellipsis_0.3.2 fitdistrplus_1.1-11
## [79] ROCR_1.0-11 nlme_3.1-164
## [81] RcppAnnoy_0.0.21 bslib_0.6.1
## [83] irlba_2.3.5.1 KernSmooth_2.23-22
## [85] colorspace_2.1-0 Exact_3.2
## [87] tidyselect_1.2.0 compiler_4.3.2
## [89] rvest_1.0.3 expm_0.999-8
## [91] xml2_1.3.6 DelayedArray_0.28.0
## [93] scales_1.3.0 caTools_1.18.2
## [95] lmtest_0.9-40 stringr_1.5.1
## [97] digest_0.6.33 goftest_1.2-3
## [99] presto_1.0.0 spatstat.utils_3.0-4
## [101] rmarkdown_2.25 RhpcBLASctl_0.23-42
## [103] XVector_0.42.0 htmltools_0.5.7
## [105] pkgconfig_2.0.3 highr_0.10
## [107] fastmap_1.1.1 rlang_1.1.2
## [109] GlobalOptions_0.1.2 htmlwidgets_1.6.4
## [111] shiny_1.8.0 jquerylib_0.1.4
## [113] farver_2.1.1 zoo_1.8-12
## [115] jsonlite_1.8.8 mclust_6.0.1
## [117] R.oo_1.25.0 RCurl_1.98-1.13
## [119] magrittr_2.0.3 GenomeInfoDbData_1.2.11
## [121] dotCall64_1.1-1 munsell_0.5.0
## [123] stringi_1.8.2 rootSolve_1.8.2.4
## [125] zlibbioc_1.48.0 MASS_7.3-60
## [127] plyr_1.8.9 parallel_4.3.2
## [129] listenv_0.9.0 lmom_3.0
## [131] deldir_2.0-2 splines_4.3.2
## [133] tensor_1.5 circlize_0.4.15
## [135] locfit_1.5-9.8 spatstat.geom_3.2-7
## [137] markdown_1.12 RcppHNSW_0.5.0
## [139] reshape2_1.4.4 evaluate_0.23
## [141] foreach_1.5.2 httpuv_1.6.13
## [143] RANN_2.6.1 tidyr_1.3.0
## [145] getopt_1.20.4 polyclip_1.10-6
## [147] future_1.33.0 clue_0.3-65
## [149] scattermore_1.2 xtable_1.8-4
## [151] e1071_1.7-14 RSpectra_0.16-1
## [153] later_1.3.2 viridisLite_0.4.2
## [155] class_7.3-22 tibble_3.2.1
## [157] cluster_2.1.6 globals_0.16.2