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 | sample_based | none | none | 500 | 20 | 250 | 3 | 0.25 | LogNormalize | 1e+06 | 2000 | 30 | 20 | 0.7 | sample | FALSE | 30 | 0.25 | 0.5 | TRUE | wilcox | 25 | FALSE | none | 1000 | 50 | 1.0.0 |
sample | disease | tissue | sample_id |
---|---|---|---|
copd.o1 | copd | lung | S1 |
copd.o2 | copd | lung | S2 |
copd.o3 | copd | lung | S3 |
ctl.o1 | control | lung | S4 |
ctl.o2 | control | lung | S5 |
ctl.o3 | control | lung | S6 |
ctl.y1 | control | lung | S7 |
ctl.y2 | control | lung | S8 |
ctl.y3 | control | lung | S9 |
sample_id | pre_qc_gene | pre_qc_cell | post_qc_gene | post_qc_cell |
---|---|---|---|---|
S1 | 24,305 | 7,653 | 19,788 | 6,705 |
S2 | 24,473 | 6,941 | 20,230 | 6,201 |
S3 | 25,665 | 7,088 | 21,087 | 6,153 |
S4 | 22,824 | 6,347 | 18,503 | 5,228 |
S5 | 22,805 | 7,368 | 18,308 | 6,012 |
S6 | 24,943 | 8,268 | 20,630 | 7,133 |
S7 | 24,143 | 6,357 | 19,876 | 5,025 |
S8 | 25,453 | 8,229 | 21,004 | 7,338 |
S9 | 24,531 | 8,359 | 20,221 | 7,283 |
Vales are post-QC.
sample_id | min | 0% | 25% | 50% | 75% | 100% | max |
---|---|---|---|---|---|---|---|
S1 | 503 | 503 | 2,258 | 3,726 | 6,061 | 37,537 | 37,537 |
S2 | 500 | 500 | 2,221 | 5,365 | 14,444 | 59,039 | 59,039 |
S3 | 501 | 501 | 1,937 | 3,123 | 8,678 | 81,770 | 81,770 |
S4 | 517 | 517 | 2,639 | 5,852 | 9,454 | 32,728 | 32,728 |
S5 | 500 | 500 | 2,362 | 4,902 | 8,031 | 26,993 | 26,993 |
S6 | 501 | 501 | 1,519 | 2,619 | 6,397 | 82,265 | 82,265 |
S7 | 500 | 500 | 2,613 | 4,569 | 8,241 | 44,545 | 44,545 |
S8 | 502 | 502 | 2,421 | 3,977 | 12,838 | 76,136 | 76,136 |
S9 | 501 | 501 | 2,159 | 8,042 | 14,097 | 39,383 | 39,383 |
Vales are post-QC.
sample_id | min | 0% | 25% | 50% | 75% | 100% | max |
---|---|---|---|---|---|---|---|
S1 | 307 | 307 | 993 | 1,382 | 1,914 | 6,359 | 6,359 |
S2 | 306 | 306 | 1,070 | 1,966 | 3,586 | 6,991 | 6,991 |
S3 | 303 | 303 | 878 | 1,309 | 2,611 | 8,448 | 8,448 |
S4 | 303 | 303 | 1,159 | 1,984 | 2,706 | 5,307 | 5,307 |
S5 | 301 | 301 | 1,060 | 1,798 | 2,468 | 4,941 | 4,941 |
S6 | 301 | 301 | 780 | 1,188 | 2,085 | 9,526 | 9,526 |
S7 | 309 | 309 | 1,081 | 1,647 | 2,645 | 7,173 | 7,173 |
S8 | 313 | 313 | 1,151 | 1,715 | 3,472 | 7,506 | 7,506 |
S9 | 305 | 305 | 1,082 | 2,597 | 3,604 | 6,923 | 6,923 |
Vales are post-QC.
sample_id | min | 0% | 25% | 50% | 75% | 100% | max |
---|---|---|---|---|---|---|---|
S1 | 0.00 | 0.00 | 7.75 | 10.30 | 13.30 | 20.00 | 20.00 |
S2 | 0.00 | 0.00 | 6.62 | 8.77 | 11.51 | 20.00 | 20.00 |
S3 | 0.10 | 0.10 | 6.95 | 9.87 | 13.20 | 20.00 | 20.00 |
S4 | 0.22 | 0.22 | 7.10 | 9.82 | 13.28 | 19.96 | 19.96 |
S5 | 0.00 | 0.00 | 8.19 | 10.57 | 13.48 | 20.00 | 20.00 |
S6 | 0.00 | 0.00 | 7.06 | 9.99 | 13.43 | 20.00 | 20.00 |
S7 | 0.00 | 0.00 | 8.60 | 11.27 | 14.48 | 20.00 | 20.00 |
S8 | 0.00 | 0.00 | 6.33 | 9.02 | 11.94 | 19.98 | 19.98 |
S9 | 0.06 | 0.06 | 8.16 | 10.19 | 12.77 | 20.00 | 20.00 |
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.
## [1] "No overlaps among samples' barcodes."
Fraction (%) of barcodes shared between pairs of samples post-QC.
## [1] "No overlaps among samples' barcodes."
Observed scores are used for doublet classification. Dashed line indicates the threshold used to identify doublets.
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 | 500 | 500 | 2,122 | 4,288 | 9,324 | 82,265 | 82,265 |
Gene per cell | 301 | 301 | 994 | 1,633 | 2,738 | 9,526 | 9,526 |
Mitochondrial (%) per cell | 0.00 | 0.00 | 7.40 | 9.93 | 13.05 | 20.00 | 20.00 |
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