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 | 750 | 10 | 250 | 3 | 0.25 | LogNormalize | 1e+06 | 2000 | 30 | 20 | 0.7 | sample | FALSE | 30 | 0.25 | 0.5 | TRUE | wilcox | 0 | FALSE | none | 1000 | 50 | 1.0.0 |
sample | disease | tissue | sample_id |
---|---|---|---|
Donor_01 | Control | Lung | S1 |
Donor_02 | Control | Lung | S2 |
Donor_03 | Control | Lung | S3 |
Donor_04 | Control | Lung | S4 |
Donor_05 | Control | Lung | S5 |
Donor_06 | Control | Lung | S6 |
Donor_07 | Control | Lung | S7 |
Donor_08 | Control | Lung | S8 |
HP_01 | HP | Lung | S9 |
IPF_01 | IPF | Lung | S10 |
IPF_02 | IPF | Lung | S11 |
IPF_03 | IPF | Lung | S12 |
IPF_04 | IPF | Lung | S13 |
Myositis-ILD_01 | Myositis-ILD | Lung | S14 |
SSc-ILD_01 | SSC-ILD | Lung | S15 |
SSc-ILD_02 | SSC-ILD | Lung | S16 |
sample_id | pre_qc_gene | pre_qc_cell | post_qc_gene | post_qc_cell |
---|---|---|---|---|
S1 | 22,709 | 323,445 | 19,334 | 5,969 |
S2 | 23,287 | 286,796 | 19,799 | 5,524 |
S3 | 22,161 | 267,010 | 18,037 | 5,594 |
S4 | 22,690 | 291,972 | 18,525 | 6,104 |
S5 | 23,402 | 349,023 | 20,098 | 8,418 |
S6 | 23,442 | 321,052 | 19,753 | 6,451 |
S7 | 23,395 | 445,249 | 19,803 | 11,457 |
S8 | 23,141 | 489,101 | 19,030 | 12,211 |
S9 | 23,573 | 335,067 | 20,075 | 5,096 |
S10 | 23,661 | 288,822 | 19,974 | 4,251 |
S11 | 23,725 | 230,383 | 19,602 | 3,125 |
S12 | 23,437 | 257,411 | 19,672 | 7,282 |
S13 | 20,501 | 202,793 | 15,812 | 3,769 |
S14 | 24,103 | 560,513 | 20,146 | 7,494 |
S15 | 23,247 | 308,872 | 19,573 | 6,489 |
S16 | 23,319 | 480,462 | 19,524 | 8,187 |
Vales are post-QC.
sample_id | min | 0% | 25% | 50% | 75% | 100% | max |
---|---|---|---|---|---|---|---|
S1 | 751 | 751 | 5,002 | 8,759 | 12,133 | 67,990 | 67,990 |
S2 | 752 | 752 | 1,956 | 5,691 | 9,702 | 79,144 | 79,144 |
S3 | 750 | 750 | 2,113 | 3,605 | 4,781 | 49,738 | 49,738 |
S4 | 751 | 751 | 1,853 | 3,068 | 4,165 | 45,129 | 45,129 |
S5 | 750 | 750 | 2,007 | 5,867 | 9,452 | 147,418 | 147,418 |
S6 | 750 | 750 | 1,814 | 4,815 | 10,860 | 100,271 | 100,271 |
S7 | 750 | 750 | 1,687 | 3,562 | 7,152 | 79,072 | 79,072 |
S8 | 750 | 750 | 1,048 | 1,850 | 4,646 | 36,934 | 36,934 |
S9 | 753 | 753 | 2,501 | 4,263 | 11,952 | 89,014 | 89,014 |
S10 | 752 | 752 | 2,206 | 3,245 | 8,295 | 99,762 | 99,762 |
S11 | 750 | 750 | 1,740 | 2,601 | 6,654 | 114,246 | 114,246 |
S12 | 750 | 750 | 1,155 | 2,188 | 4,410 | 61,631 | 61,631 |
S13 | 750 | 750 | 1,013 | 1,312 | 1,851 | 11,201 | 11,201 |
S14 | 750 | 750 | 2,193 | 4,011 | 6,795 | 44,130 | 44,130 |
S15 | 750 | 750 | 1,845 | 4,231 | 7,591 | 64,310 | 64,310 |
S16 | 750 | 750 | 2,341 | 5,223 | 9,735 | 61,082 | 61,082 |
Vales are post-QC.
sample_id | min | 0% | 25% | 50% | 75% | 100% | max |
---|---|---|---|---|---|---|---|
S1 | 336 | 336 | 1,564 | 2,195 | 2,665 | 8,165 | 8,165 |
S2 | 269 | 269 | 920 | 1,801 | 2,479 | 9,139 | 9,139 |
S3 | 307 | 307 | 967 | 1,384 | 1,685 | 7,832 | 7,832 |
S4 | 280 | 280 | 846 | 1,162 | 1,469 | 7,622 | 7,622 |
S5 | 257 | 257 | 876 | 1,659 | 2,247 | 8,696 | 8,696 |
S6 | 269 | 269 | 846 | 1,693 | 2,722 | 10,045 | 10,045 |
S7 | 251 | 251 | 756 | 1,247 | 1,905 | 6,514 | 6,514 |
S8 | 277 | 277 | 526 | 807 | 1,522 | 6,114 | 6,114 |
S9 | 255 | 255 | 970 | 1,435 | 2,778 | 9,598 | 9,598 |
S10 | 253 | 253 | 874 | 1,151 | 2,139 | 8,858 | 8,858 |
S11 | 251 | 251 | 745 | 1,050 | 2,093 | 9,743 | 9,743 |
S12 | 251 | 251 | 528 | 846 | 1,491 | 7,996 | 7,996 |
S13 | 250 | 250 | 548 | 671 | 888 | 3,515 | 3,515 |
S14 | 274 | 274 | 933 | 1,416 | 1,968 | 6,307 | 6,307 |
S15 | 267 | 267 | 844 | 1,524 | 2,150 | 7,946 | 7,946 |
S16 | 285 | 285 | 968 | 1,606 | 2,336 | 8,860 | 8,860 |
Vales are post-QC.
sample_id | min | 0% | 25% | 50% | 75% | 100% | max |
---|---|---|---|---|---|---|---|
S1 | 0.09 | 0.09 | 4.90 | 5.93 | 7.02 | 9.97 | 9.97 |
S2 | 0.00 | 0.00 | 3.88 | 4.83 | 5.87 | 9.99 | 9.99 |
S3 | 0.17 | 0.17 | 3.17 | 4.17 | 5.34 | 9.99 | 9.99 |
S4 | 0.12 | 0.12 | 4.45 | 5.51 | 6.74 | 10.00 | 10.00 |
S5 | 0.00 | 0.00 | 2.32 | 3.07 | 4.08 | 9.95 | 9.95 |
S6 | 0.00 | 0.00 | 3.00 | 3.91 | 5.01 | 10.00 | 10.00 |
S7 | 0.13 | 0.13 | 1.89 | 2.59 | 3.68 | 9.99 | 9.99 |
S8 | 0.00 | 0.00 | 2.83 | 3.97 | 5.39 | 10.00 | 10.00 |
S9 | 0.00 | 0.00 | 3.26 | 4.42 | 5.82 | 9.99 | 9.99 |
S10 | 0.00 | 0.00 | 3.80 | 4.80 | 6.13 | 9.99 | 9.99 |
S11 | 0.13 | 0.13 | 2.60 | 3.53 | 4.86 | 10.00 | 10.00 |
S12 | 0.27 | 0.27 | 4.33 | 5.81 | 7.24 | 10.00 | 10.00 |
S13 | 0.90 | 0.90 | 5.08 | 6.41 | 7.80 | 9.99 | 9.99 |
S14 | 0.71 | 0.71 | 3.81 | 5.13 | 6.82 | 10.00 | 10.00 |
S15 | 0.45 | 0.45 | 3.89 | 5.05 | 6.47 | 10.00 | 10.00 |
S16 | 0.12 | 0.12 | 3.56 | 4.44 | 5.50 | 10.00 | 10.00 |
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.
S1 | S2 | S3 | S4 | S5 | S6 | S7 | S8 | S9 | S10 | S11 | S12 | S13 | S14 | S15 | S16 | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
S1 | 5969 | 45 | 52 | 45 | 60 | 49 | 90 | 91 | 39 | 34 | 30 | 45 | 34 | 64 | 56 | 79 |
S2 | 45 | 5524 | 42 | 55 | 52 | 47 | 87 | 86 | 29 | 28 | 14 | 58 | 33 | 72 | 57 | 69 |
S3 | 52 | 42 | 5594 | 43 | 60 | 50 | 91 | 81 | 37 | 31 | 27 | 67 | 31 | 61 | 57 | 65 |
S4 | 45 | 55 | 43 | 6104 | 71 | 58 | 93 | 79 | 36 | 36 | 24 | 275 | 32 | 66 | 60 | 84 |
S5 | 60 | 52 | 60 | 71 | 8418 | 65 | 134 | 131 | 65 | 51 | 37 | 86 | 50 | 60 | 89 | 84 |
S6 | 49 | 47 | 50 | 58 | 65 | 6451 | 113 | 108 | 44 | 36 | 26 | 68 | 39 | 81 | 65 | 72 |
S7 | 90 | 87 | 91 | 93 | 134 | 113 | 11457 | 837 | 67 | 75 | 53 | 131 | 51 | 137 | 102 | 463 |
S8 | 91 | 86 | 81 | 79 | 131 | 108 | 837 | 12211 | 101 | 85 | 51 | 116 | 52 | 129 | 88 | 459 |
S9 | 39 | 29 | 37 | 36 | 65 | 44 | 67 | 101 | 5096 | 28 | 17 | 40 | 23 | 45 | 36 | 54 |
S10 | 34 | 28 | 31 | 36 | 51 | 36 | 75 | 85 | 28 | 4251 | 21 | 47 | 21 | 43 | 35 | 58 |
S11 | 30 | 14 | 27 | 24 | 37 | 26 | 53 | 51 | 17 | 21 | 3125 | 28 | 17 | 35 | 22 | 31 |
S12 | 45 | 58 | 67 | 275 | 86 | 68 | 131 | 116 | 40 | 47 | 28 | 7282 | 40 | 62 | 61 | 76 |
S13 | 34 | 33 | 31 | 32 | 50 | 39 | 51 | 52 | 23 | 21 | 17 | 40 | 3769 | 43 | 33 | 36 |
S14 | 64 | 72 | 61 | 66 | 60 | 81 | 137 | 129 | 45 | 43 | 35 | 62 | 43 | 7494 | 69 | 89 |
S15 | 56 | 57 | 57 | 60 | 89 | 65 | 102 | 88 | 36 | 35 | 22 | 61 | 33 | 69 | 6489 | 73 |
S16 | 79 | 69 | 65 | 84 | 84 | 72 | 463 | 459 | 54 | 58 | 31 | 76 | 36 | 89 | 73 | 8187 |
Fraction (%) of barcodes shared between pairs of samples post-QC.
S1 | S2 | S3 | S4 | S5 | S6 | S7 | S8 | S9 | S10 | S11 | S12 | S13 | S14 | S15 | S16 | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
S1 | 100.00 | 0.78 | 0.90 | 0.75 | 0.83 | 0.79 | 1.03 | 1.00 | 0.70 | 0.67 | 0.66 | 0.68 | 0.70 | 0.95 | 0.90 | 1.12 |
S2 | 0.78 | 100.00 | 0.76 | 0.95 | 0.75 | 0.78 | 1.02 | 0.97 | 0.55 | 0.57 | 0.32 | 0.91 | 0.71 | 1.11 | 0.95 | 1.01 |
S3 | 0.90 | 0.76 | 100.00 | 0.74 | 0.86 | 0.83 | 1.07 | 0.91 | 0.69 | 0.63 | 0.62 | 1.04 | 0.66 | 0.93 | 0.94 | 0.94 |
S4 | 0.75 | 0.95 | 0.74 | 100.00 | 0.98 | 0.92 | 1.06 | 0.86 | 0.64 | 0.70 | 0.52 | 4.11 | 0.65 | 0.97 | 0.95 | 1.18 |
S5 | 0.83 | 0.75 | 0.86 | 0.98 | 100.00 | 0.87 | 1.35 | 1.27 | 0.96 | 0.81 | 0.64 | 1.10 | 0.82 | 0.75 | 1.19 | 1.01 |
S6 | 0.79 | 0.78 | 0.83 | 0.92 | 0.87 | 100.00 | 1.26 | 1.16 | 0.76 | 0.67 | 0.54 | 0.99 | 0.76 | 1.16 | 1.00 | 0.98 |
S7 | 1.03 | 1.02 | 1.07 | 1.06 | 1.35 | 1.26 | 100.00 | 7.07 | 0.81 | 0.95 | 0.73 | 1.40 | 0.67 | 1.45 | 1.14 | 4.71 |
S8 | 1.00 | 0.97 | 0.91 | 0.86 | 1.27 | 1.16 | 7.07 | 100.00 | 1.17 | 1.03 | 0.67 | 1.19 | 0.65 | 1.31 | 0.94 | 4.50 |
S9 | 0.70 | 0.55 | 0.69 | 0.64 | 0.96 | 0.76 | 0.81 | 1.17 | 100.00 | 0.60 | 0.41 | 0.65 | 0.52 | 0.71 | 0.62 | 0.81 |
S10 | 0.67 | 0.57 | 0.63 | 0.70 | 0.81 | 0.67 | 0.95 | 1.03 | 0.60 | 100.00 | 0.57 | 0.82 | 0.52 | 0.73 | 0.65 | 0.93 |
S11 | 0.66 | 0.32 | 0.62 | 0.52 | 0.64 | 0.54 | 0.73 | 0.67 | 0.41 | 0.57 | 100.00 | 0.54 | 0.49 | 0.66 | 0.46 | 0.55 |
S12 | 0.68 | 0.91 | 1.04 | 4.11 | 1.10 | 0.99 | 1.40 | 1.19 | 0.65 | 0.82 | 0.54 | 100.00 | 0.72 | 0.84 | 0.89 | 0.98 |
S13 | 0.70 | 0.71 | 0.66 | 0.65 | 0.82 | 0.76 | 0.67 | 0.65 | 0.52 | 0.52 | 0.49 | 0.72 | 100.00 | 0.76 | 0.64 | 0.60 |
S14 | 0.95 | 1.11 | 0.93 | 0.97 | 0.75 | 1.16 | 1.45 | 1.31 | 0.71 | 0.73 | 0.66 | 0.84 | 0.76 | 100.00 | 0.99 | 1.14 |
S15 | 0.90 | 0.95 | 0.94 | 0.95 | 1.19 | 1.00 | 1.14 | 0.94 | 0.62 | 0.65 | 0.46 | 0.89 | 0.64 | 0.99 | 100.00 | 0.99 |
S16 | 1.12 | 1.01 | 0.94 | 1.18 | 1.01 | 0.98 | 4.71 | 4.50 | 0.81 | 0.93 | 0.55 | 0.98 | 0.60 | 1.14 | 0.99 | 100.00 |
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 | 750 | 750 | 1,662 | 3,555 | 7,532 | 147,418 | 147,418 |
Gene per cell | 250 | 250 | 755 | 1,301 | 2,068 | 10,045 | 10,045 |
Mitochondrial (%) per cell | 0.00 | 0.00 | 3.12 | 4.43 | 5.95 | 10.00 | 10.00 |
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.
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] 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 lifecycle_1.0.4
## [43] yaml_2.3.7 SparseArray_1.2.2
## [45] Rtsne_0.17 promises_1.2.1
## [47] crayon_1.5.2 miniUI_0.1.1.1
## [49] lattice_0.22-5 pillar_1.9.0
## [51] knitr_1.45 rjson_0.2.21
## [53] boot_1.3-28.1 gld_2.6.6
## [55] future.apply_1.11.0 codetools_0.2-19
## [57] leiden_0.4.3.1 glue_1.6.2
## [59] vctrs_0.6.5 png_0.1-8
## [61] spam_2.10-0 neuralnet_1.44.2
## [63] cellranger_1.1.0 gtable_0.3.4
## [65] cachem_1.0.8 ks_1.14.1
## [67] xfun_0.41 S4Arrays_1.2.0
## [69] mime_0.12 pracma_2.4.4
## [71] survival_3.5-7 pbmcapply_1.5.1
## [73] iterators_1.0.14 statmod_1.5.0
## [75] ellipsis_0.3.2 fitdistrplus_1.1-11
## [77] ROCR_1.0-11 nlme_3.1-164
## [79] bit64_4.0.5 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] bit_4.0.5 compiler_4.3.2
## [89] rvest_1.0.3 hdf5r_1.3.8
## [91] expm_0.999-8 xml2_1.3.6
## [93] DelayedArray_0.28.0 scales_1.3.0
## [95] caTools_1.18.2 lmtest_0.9-40
## [97] stringr_1.5.1 digest_0.6.33
## [99] goftest_1.2-3 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] RCurl_1.98-1.13 magrittr_2.0.3
## [119] GenomeInfoDbData_1.2.11 dotCall64_1.1-1
## [121] munsell_0.5.0 stringi_1.8.2
## [123] rootSolve_1.8.2.4 zlibbioc_1.48.0
## [125] MASS_7.3-60 plyr_1.8.9
## [127] parallel_4.3.2 listenv_0.9.0
## [129] lmom_3.0 deldir_2.0-2
## [131] splines_4.3.2 tensor_1.5
## [133] circlize_0.4.15 locfit_1.5-9.8
## [135] spatstat.geom_3.2-7 markdown_1.12
## [137] RcppHNSW_0.5.0 reshape2_1.4.4
## [139] evaluate_0.23 foreach_1.5.2
## [141] httpuv_1.6.13 RANN_2.6.1
## [143] tidyr_1.3.0 getopt_1.20.4
## [145] polyclip_1.10-6 future_1.33.0
## [147] clue_0.3-65 scattermore_1.2
## [149] xtable_1.8-4 e1071_1.7-14
## [151] RSpectra_0.16-1 later_1.3.2
## [153] viridisLite_0.4.2 class_7.3-22
## [155] tibble_3.2.1 cluster_2.1.6
## [157] globals_0.16.2