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 | kidney | cell_based | none | none | 750 | 10 | 250 | 3 | 0 | LogNormalize | 1e+06 | 2000 | 30 | 20 | 0.7 | none | FALSE | 30 | 0.25 | 0.5 | TRUE | wilcox | 25 | FALSE | none | 1000 | 50 | 1.0.0 |
sample | sample_id | tissue | disease | gender | age | ckd.stage | x24.h.proteinuria..g. |
---|---|---|---|---|---|---|---|
CombinedData | S1 | Kidney | IgAN,normal control | male,female | 44,34,51,28,31,55,27,19,40,30,29,37,50,25,65,68 | 2,1,3a | 1.3,3,1.1,0.5,1.9,1.4,5.1,1.6,1,2.4,none |
sample_id | pre_qc_gene | pre_qc_cell | post_qc_gene | post_qc_cell |
---|---|---|---|---|
S1 | 22,037 | 2,785 | 20,355 | 2,785 |
Vales are post-QC.
sample_id | min | 0% | 25% | 50% | 75% | 100% | max |
---|---|---|---|---|---|---|---|
S1 | 4,011 | 4,011 | 11,196 | 21,755 | 45,069 | 524,975 | 524,975 |
Vales are post-QC.
sample_id | min | 0% | 25% | 50% | 75% | 100% | max |
---|---|---|---|---|---|---|---|
S1 | 1,002 | 1,002 | 2,123 | 2,942 | 4,136 | 11,556 | 11,556 |
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 | 2785 |
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 | 4,011 | 4,011 | 11,196 | 21,755 | 45,069 | 524,975 | 524,975 |
Gene per cell | 1,002 | 1,002 | 2,123 | 2,942 | 4,136 | 11,556 | 11,556 |
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 lazyeval_0.2.2
## [11] uwot_0.1.16 ggdist_3.3.1
## [13] GetoptLong_1.0.5 withr_2.5.2
## [15] progressr_0.14.0 cli_3.6.1
## [17] spatstat.explore_3.2-5 fastDummies_1.7.3
## [19] labeling_0.4.3 sass_0.4.8
## [21] mvtnorm_1.2-4 spatstat.data_3.0-3
## [23] proxy_0.4-27 ggridges_0.5.4
## [25] pbapply_1.7-2 commonmark_1.9.0
## [27] systemfonts_1.0.5 R.utils_2.12.3
## [29] svglite_2.1.2 parallelly_1.36.0
## [31] rstudioapi_0.15.0 generics_0.1.3
## [33] shape_1.4.6 crosstalk_1.2.1
## [35] ica_1.0-3 spatstat.random_3.2-2
## [37] distributional_0.3.2 Matrix_1.6-4
## [39] fansi_1.0.6 DescTools_0.99.52
## [41] abind_1.4-5 R.methodsS3_1.8.2
## [43] lifecycle_1.0.4 yaml_2.3.7
## [45] SparseArray_1.2.2 Rtsne_0.17
## [47] promises_1.2.1 crayon_1.5.2
## [49] miniUI_0.1.1.1 lattice_0.22-5
## [51] pillar_1.9.0 knitr_1.45
## [53] rjson_0.2.21 boot_1.3-28.1
## [55] gld_2.6.6 future.apply_1.11.0
## [57] codetools_0.2-19 leiden_0.4.3.1
## [59] glue_1.6.2 vctrs_0.6.5
## [61] png_0.1-8 spam_2.10-0
## [63] neuralnet_1.44.2 cellranger_1.1.0
## [65] gtable_0.3.4 cachem_1.0.8
## [67] ks_1.14.1 xfun_0.41
## [69] S4Arrays_1.2.0 mime_0.12
## [71] pracma_2.4.4 survival_3.5-7
## [73] pbmcapply_1.5.1 iterators_1.0.14
## [75] statmod_1.5.0 ellipsis_0.3.2
## [77] fitdistrplus_1.1-11 ROCR_1.0-11
## [79] nlme_3.1-164 RcppAnnoy_0.0.21
## [81] bslib_0.6.1 irlba_2.3.5.1
## [83] KernSmooth_2.23-22 colorspace_2.1-0
## [85] Exact_3.2 tidyselect_1.2.0
## [87] compiler_4.3.2 rvest_1.0.3
## [89] expm_0.999-8 xml2_1.3.6
## [91] DelayedArray_0.28.0 scales_1.3.0
## [93] caTools_1.18.2 lmtest_0.9-40
## [95] stringr_1.5.1 digest_0.6.33
## [97] goftest_1.2-3 presto_1.0.0
## [99] spatstat.utils_3.0-4 rmarkdown_2.25
## [101] XVector_0.42.0 htmltools_0.5.7
## [103] pkgconfig_2.0.3 highr_0.10
## [105] fastmap_1.1.1 rlang_1.1.2
## [107] GlobalOptions_0.1.2 htmlwidgets_1.6.4
## [109] shiny_1.8.0 jquerylib_0.1.4
## [111] farver_2.1.1 zoo_1.8-12
## [113] jsonlite_1.8.8 mclust_6.0.1
## [115] R.oo_1.25.0 RCurl_1.98-1.13
## [117] magrittr_2.0.3 GenomeInfoDbData_1.2.11
## [119] dotCall64_1.1-1 munsell_0.5.0
## [121] stringi_1.8.2 rootSolve_1.8.2.4
## [123] zlibbioc_1.48.0 MASS_7.3-60
## [125] plyr_1.8.9 parallel_4.3.2
## [127] listenv_0.9.0 lmom_3.0
## [129] deldir_2.0-2 splines_4.3.2
## [131] tensor_1.5 circlize_0.4.15
## [133] locfit_1.5-9.8 spatstat.geom_3.2-7
## [135] markdown_1.12 RcppHNSW_0.5.0
## [137] reshape2_1.4.4 evaluate_0.23
## [139] foreach_1.5.2 httpuv_1.6.13
## [141] RANN_2.6.1 tidyr_1.3.0
## [143] getopt_1.20.4 polyclip_1.10-6
## [145] future_1.33.0 clue_0.3-65
## [147] scattermore_1.2 xtable_1.8-4
## [149] e1071_1.7-14 RSpectra_0.16-1
## [151] later_1.3.2 viridisLite_0.4.2
## [153] class_7.3-22 tibble_3.2.1
## [155] cluster_2.1.6 globals_0.16.2