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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 skin sample_based none none 0 20 200 3 0 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

3 BridgeQC

3.1 Sample-level Characteristics and Quality Control Metrics

3.1.1 Metadata

sample disease tissue sample_id
S10_H Healthy Skin S1
S11_NL Non Lesional Skin S2
S12_H Healthy Skin S3
S13_H Healthy Skin S4
S14_NL Non Lesional Skin S5
S15_NL Non Lesional Skin S6
S16_NL Non Lesional Skin S7
S17_H Healthy Skin S8
S1_LS Lesional Skin S9
S2_LS Lesional Skin S10
S3_NL Non Lesional Skin S11
S4_H Healthy Skin S12
S5_LS Lesional Skin S13
S6_H Healthy Skin S14
S7_LS Lesional Skin S15
S8_H Healthy Skin S16
S9_H Healthy Skin S17

3.1.2 Cells & Genes (RNA)

sample_id pre_qc_gene pre_qc_cell post_qc_gene post_qc_cell
S1 14,635 1,359 14,388 1,032
S2 14,862 3,125 14,801 2,680
S3 15,931 2,981 15,741 2,409
S4 11,465 339 11,194 258
S5 14,805 854 14,486 815
S6 11,307 635 11,076 473
S7 9,462 119 9,424 107
S8 14,706 1,605 14,555 1,277
S9 16,165 4,147 16,113 3,904
S10 15,807 1,971 15,516 1,836
S11 15,106 2,302 14,997 2,083
S12 16,961 4,760 16,824 4,745
S13 15,383 2,578 15,349 2,328
S14 14,778 4,541 14,604 3,320
S15 14,814 1,737 14,758 1,546
S16 16,193 4,740 16,146 4,431
S17 16,717 7,539 16,627 6,355

3.1.3 UMI per cell (RNA)

Vales are post-QC.

sample_id min 0% 25% 50% 75% 100% max
S1 484 484 1,065 1,477 2,015 4,321 4,321
S2 348 348 1,123 1,484 1,952 4,090 4,090
S3 761 761 1,162 1,592 2,338 4,307 4,307
S4 602 602 1,201 1,575 2,154 4,057 4,057
S5 777 777 1,712 2,287 2,786 4,208 4,208
S6 768 768 986 1,235 1,648 3,978 3,978
S7 782 782 1,362 1,706 2,393 4,058 4,058
S8 710 710 1,103 1,525 2,085 4,062 4,062
S9 750 750 1,303 1,769 2,265 4,162 4,162
S10 690 690 1,508 2,081 2,641 4,480 4,480
S11 761 761 1,306 1,734 2,226 4,161 4,161
S12 643 643 1,578 1,915 2,375 4,401 4,401
S13 756 756 1,340 1,760 2,237 3,969 3,969
S14 747 747 1,036 1,359 1,765 3,844 3,844
S15 508 508 1,279 1,711 2,226 4,171 4,171
S16 519 519 2,296 2,652 2,964 4,153 4,153
S17 498 498 1,066 1,436 2,032 4,212 4,212

3.1.4 Gene per cell (RNA)

Vales are post-QC.

sample_id min 0% 25% 50% 75% 100% max
S1 200 200 323 518 855 4,599 4,599
S2 200 200 339 524 827 4,843 4,843
S3 200 200 357 579 1,124 4,937 4,937
S4 200 200 369 561 981 3,940 3,940
S5 200 200 663 1,103 1,684 4,954 4,954
S6 200 200 282 393 609 4,271 4,271
S7 206 206 449 669 1,246 4,249 4,249
S8 200 200 338 547 925 4,896 4,896
S9 200 200 429 706 1,117 4,824 4,824
S10 200 200 530 924 1,530 4,953 4,953
S11 200 200 422 668 1,039 4,831 4,831
S12 200 200 564 783 1,164 4,959 4,959
S13 200 200 448 693 1,103 4,663 4,663
S14 200 200 302 453 693 4,007 4,007
S15 200 200 422 659 1,092 4,946 4,946
S16 200 200 1,111 1,521 2,007 4,975 4,975
S17 200 200 315 489 871 4,962 4,962

3.1.5 Mitochondrial (%) per cell (RNA)

Vales are post-QC.

sample_id min 0% 25% 50% 75% 100% max
S1 0 0 0 0 0 0 0
S2 0 0 0 0 0 0 0
S3 0 0 0 0 0 0 0
S4 0 0 0 0 0 0 0
S5 0 0 0 0 0 0 0
S6 0 0 0 0 0 0 0
S7 0 0 0 0 0 0 0
S8 0 0 0 0 0 0 0
S9 0 0 0 0 0 0 0
S10 0 0 0 0 0 0 0
S11 0 0 0 0 0 0 0
S12 0 0 0 0 0 0 0
S13 0 0 0 0 0 0 0
S14 0 0 0 0 0 0 0
S15 0 0 0 0 0 0 0
S16 0 0 0 0 0 0 0
S17 0 0 0 0 0 0 0


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.

## [1] "No overlaps among samples' barcodes."

3.3.2 Jaccard Index

Fraction (%) of barcodes shared between pairs of samples post-QC.

## [1] "No overlaps among samples' barcodes."


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

3.4.1 Number of cells and genes per sample

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

3.4.2 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.


3.5 Key QC Metrics of Merged Samples

min 0% 25% 50% 75% 100% max
UMI per cell 348 348 1,257 1,743 2,384 4,480 4,480
Gene per cell 200 200 403 676 1,202 4,975 4,975
Mitochondrial (%) per cell 0 0 0 0 0 0 0

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

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

3.6.1 Signacx SPRING Projection

3.6.2 Seurat UMAP Projection

3.6.3 Seurat UMAP Projection (no-batch correction)


3.7 Visualizing Cell QC Metrics Using Dimensionality Reduction

Note: QC metrics based on kernel density estimation.

3.7.1 UMI per cell - SPRING Projection

3.7.2 Gene per cell - SPRING Projection

3.7.3 UMI per cell - UMAP Projection

3.7.4 Gene 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


4.4 Markers per cluster

Top 25 differentially expressed genes ( p_val_adj < 0.05 and pct.1 > 0.5 ) for each of the clusters

4.4.1 Seurat

4.4.2 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] 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