logo

1 Data Summary


2 BridgeSettings

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

3 BridgeQC

3.1 Sample-level Characteristics and Quality Control Metrics

3.1.1 Metadata

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

3.1.2 Cells & Genes (RNA)

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

3.1.3 UMI per cell (RNA)

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

3.1.4 Gene per cell (RNA)

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

3.1.5 Mitochondrial (%) per cell (RNA)

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


3.2 Distribution Plots for Quality Control Metrics

Thresholds, represented by dashed lines, were implemented to filter the data and only retain cells of high quality.

3.2.1 UMI per cell

3.2.2 Gene per cell

3.2.3 Mitochondrial (%) per cell


3.3 Barcodes contamination

3.3.1 Raw count

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

3.3.2 Jaccard Index

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


3.4 Doublet Cell Detection using Scrublet Scoring System

Observed scores are used for doublet classification. Dashed line indicates the threshold used to identify doublets.

3.4.1 Observed scores

3.4.2 Simulated scores


3.5 Impact of Quality Control on Cell, Gene, and UMI Abundances

3.5.1 Number of cells and genes per sample

diamonds and diamonds refer to before and after QC, respectively.

3.5.2 Average number of UMIs/cell and genes/cell per sample

The error bars represent the standard deviation of the number of UMIs and genes across cells per sample.


3.6 Key QC Metrics of Merged Samples

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

3.7 Visualization of Merged scRNA-Seq Object in 2D Space

Note: Labels may have been removed if they overlap excessively.

3.7.1 Signacx SPRING Projection

3.7.2 Seurat UMAP Projection

3.7.3 Seurat UMAP Projection (no-batch correction)


3.8 Visualizing Cell QC Metrics Using Dimensionality Reduction

Note: QC metrics based on kernel density estimation.

3.8.1 UMI per cell - SPRING Projection

3.8.2 Gene per cell - SPRING Projection

3.8.3 Mitochondrial (%) per cell - SPRING Projection

3.8.4 UMI per cell - UMAP Projection

3.8.5 Gene per cell - UMAP Projection

3.8.6 Mitochondrial (%) per cell - UMAP Projection


4 BridgeCluster

Note: Labels may have been removed if they overlap excessively.

4.1 Visualizing Cell Populations with Dimensionality Reduction

4.1.1 Signacx SPRING Projection

4.1.2 Seurat UMAP Projection


4.2 Cell Cluster Composition

Values at the top of each bar indicate the percentage of cells

4.2.1 Seurat

4.2.2 Signacx


4.3 Statistical Dispersion Analysis of Cell Composition in Clusters across sample

This measurement is a proxy to batch effect artifacts. Values adjacent to each point indicate the number of cells.

4.3.1 Seurat

4.3.2 Signacx


5 BridgeAnnotation

5.1 Visualizing Distinct Cell Type Populations with Dimensionality Reduction

Note: Labels may have been removed if they overlap excessively.

5.1.1 Signacx SPRING Projection

5.1.2 Signacx UMAP Projection


5.2 Cell Type Population Composition

Values at the top of each bar indicate the percentage of cells

5.2.1 Signacx


6 References

Publication: no referrence

Data Availability: no referrence


7 Session Information

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