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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 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 age sex ethnicity easi diagnosis sample.type tissue sample_id
AD1 18 male Caucasian 34.2 AD Suction blister Skin S1
AD2 19 female Caucasian 44.6 AD Suction blister Skin S2
AD3 34 male Caucasian 44.7 AD Suction blister Skin S3
AD4 33 female Caucasian 5.5 AD Suction blister Skin S4
AD5 31 female Caucasian 24.1 AD Biopsy Skin S5
AD6 22 male Caucasian 28.1 AD Biopsy Skin S6
AD7 24 male Caucasian 42.8 AD Biopsy Skin S7
AD8 42 male Caucasian 46.5 AD Biopsy Skin S8
HC1 40 male Caucasian n.a. HC Suction blister Skin S9
HC2 42 female Caucasian n.a. HC Suction blister Skin S10
HC3 47 female Caucasian n.a. HC Suction blister Skin S11
HC4 49 female Caucasian n.a. HC Suction blister Skin S12
HC5 39 male Caucasian n.a. HC Suction blister Skin S13
HC6 27 female Caucasian n.a. HC Biopsy Skin S14
HC7 42 female Caucasian n.a. HC Biopsy Skin S15

3.1.2 Cells & Genes (RNA)

sample_id pre_qc_gene pre_qc_cell post_qc_gene post_qc_cell
S1 18,056 4,864 14,940 3,359
S2 19,234 4,104 16,386 3,606
S3 18,905 2,084 15,994 1,657
S4 21,025 3,484 17,625 2,873
S5 21,207 5,679 17,294 3,701
S6 21,917 5,145 17,931 3,487
S7 22,126 6,321 17,858 3,246
S8 21,965 5,610 17,817 3,175
S9 10,890 171 6,351 77
S10 16,727 751 13,224 506
S11 18,190 2,709 15,041 2,030
S12 18,675 2,277 15,441 1,804
S13 19,829 915 15,761 472
S14 22,817 5,882 19,001 3,647
S15 22,913 10,588 18,981 6,667

3.1.3 UMI per cell (RNA)

Vales are post-QC.

sample_id min 0% 25% 50% 75% 100% max
S1 751 751 3,672 4,953 6,372 16,680 16,680
S2 766 766 4,284 7,388 10,348 42,398 42,398
S3 874 874 5,114 8,683 12,400 30,276 30,276
S4 797 797 4,231 8,715 12,996 56,775 56,775
S5 751 751 2,318 4,282 7,531 31,792 31,792
S6 750 750 2,070 3,604 6,968 32,552 32,552
S7 754 754 2,145 4,306 8,525 45,811 45,811
S8 754 754 2,468 4,297 6,984 41,603 41,603
S9 839 839 2,960 3,510 3,864 11,756 11,756
S10 774 774 2,814 5,208 9,583 22,936 22,936
S11 774 774 2,926 5,268 7,388 24,119 24,119
S12 891 891 4,442 7,131 9,845 26,965 26,965
S13 924 924 6,034 8,152 18,012 62,423 62,423
S14 752 752 2,352 3,976 7,068 49,824 49,824
S15 750 750 1,882 3,265 4,659 40,401 40,401

3.1.4 Gene per cell (RNA)

Vales are post-QC.

sample_id min 0% 25% 50% 75% 100% max
S1 306 306 1,060 1,266 1,484 2,623 2,623
S2 345 345 1,332 1,867 2,287 4,934 4,934
S3 464 464 1,648 2,222 2,702 4,501 4,501
S4 420 420 1,580 2,369 2,932 6,281 6,281
S5 359 359 987 1,506 2,122 4,912 4,912
S6 355 355 908 1,365 2,038 5,619 5,619
S7 355 355 944 1,548 2,353 5,970 5,970
S8 316 316 1,056 1,578 2,136 5,535 5,535
S9 345 345 941 1,068 1,188 1,979 1,979
S10 414 414 924 1,490 2,004 3,323 3,323
S11 374 374 998 1,354 1,625 3,249 3,249
S12 303 303 1,332 1,602 1,918 3,676 3,676
S13 476 476 1,946 2,418 3,534 7,822 7,822
S14 394 394 979 1,520 2,230 5,987 5,987
S15 294 294 850 1,256 1,573 4,475 4,475

3.1.5 Mitochondrial (%) per cell (RNA)

Vales are post-QC.

sample_id min 0% 25% 50% 75% 100% max
S1 0.00 0.00 2.85 3.42 4.21 10.00 10.00
S2 0.00 0.00 2.41 2.91 3.67 10.00 10.00
S3 0.00 0.00 2.05 2.54 3.29 9.98 9.98
S4 0.00 0.00 3.74 4.45 5.32 10.00 10.00
S5 0.00 0.00 3.73 4.65 5.86 10.00 10.00
S6 0.00 0.00 2.67 3.80 5.08 9.97 9.97
S7 0.00 0.00 3.55 4.70 6.11 10.00 10.00
S8 0.00 0.00 3.65 4.90 6.44 10.00 10.00
S9 0.76 0.76 2.35 3.17 3.75 9.30 9.30
S10 0.00 0.00 2.61 3.50 4.60 9.80 9.80
S11 0.00 0.00 3.21 4.31 5.72 9.84 9.84
S12 0.03 0.03 2.79 3.53 4.60 9.96 9.96
S13 0.00 0.00 4.45 5.87 7.18 9.99 9.99
S14 0.00 0.00 3.05 4.28 6.04 9.99 9.99
S15 0.00 0.00 2.57 4.00 5.81 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
S1 3359 18 7 0 0 0 0 0 3 2 7 11 0 1 1
S2 18 3606 9 0 2 0 0 0 1 6 9 13 0 1 0
S3 7 9 1657 0 0 0 0 1 0 2 3 1 0 0 0
S4 0 0 0 2873 2 3 3 1 0 0 0 0 0 4 8
S5 0 2 0 2 3701 4 6 4 0 0 2 0 1 1 13
S6 0 0 0 3 4 3487 2 3 0 0 0 0 1 2 8
S7 0 0 0 3 6 2 3246 2 0 0 0 0 0 5 6
S8 0 0 1 1 4 3 2 3175 0 0 0 0 0 4 3
S9 3 1 0 0 0 0 0 0 77 0 1 0 0 0 0
S10 2 6 2 0 0 0 0 0 0 506 2 0 0 0 0
S11 7 9 3 0 2 0 0 0 1 2 2030 4 0 0 0
S12 11 13 1 0 0 0 0 0 0 0 4 1804 0 0 0
S13 0 0 0 0 1 1 0 0 0 0 0 0 472 4 2
S14 1 1 0 4 1 2 5 4 0 0 0 0 4 3647 7
S15 1 0 0 8 13 8 6 3 0 0 0 0 2 7 6667

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
S1 100.00 0.52 0.28 0.00 0.00 0.00 0.00 0.00 0.17 0.10 0.26 0.43 0.00 0.03 0.02
S2 0.52 100.00 0.34 0.00 0.05 0.00 0.00 0.00 0.05 0.29 0.32 0.48 0.00 0.03 0.00
S3 0.28 0.34 100.00 0.00 0.00 0.00 0.00 0.04 0.00 0.18 0.16 0.06 0.00 0.00 0.00
S4 0.00 0.00 0.00 100.00 0.06 0.09 0.10 0.03 0.00 0.00 0.00 0.00 0.00 0.12 0.17
S5 0.00 0.05 0.00 0.06 100.00 0.11 0.17 0.12 0.00 0.00 0.07 0.00 0.05 0.03 0.25
S6 0.00 0.00 0.00 0.09 0.11 100.00 0.06 0.09 0.00 0.00 0.00 0.00 0.05 0.06 0.16
S7 0.00 0.00 0.00 0.10 0.17 0.06 100.00 0.06 0.00 0.00 0.00 0.00 0.00 0.15 0.12
S8 0.00 0.00 0.04 0.03 0.12 0.09 0.06 100.00 0.00 0.00 0.00 0.00 0.00 0.12 0.06
S9 0.17 0.05 0.00 0.00 0.00 0.00 0.00 0.00 100.00 0.00 0.09 0.00 0.00 0.00 0.00
S10 0.10 0.29 0.18 0.00 0.00 0.00 0.00 0.00 0.00 100.00 0.16 0.00 0.00 0.00 0.00
S11 0.26 0.32 0.16 0.00 0.07 0.00 0.00 0.00 0.09 0.16 100.00 0.21 0.00 0.00 0.00
S12 0.43 0.48 0.06 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.21 100.00 0.00 0.00 0.00
S13 0.00 0.00 0.00 0.00 0.05 0.05 0.00 0.00 0.00 0.00 0.00 0.00 100.00 0.19 0.06
S14 0.03 0.03 0.00 0.12 0.03 0.06 0.15 0.12 0.00 0.00 0.00 0.00 0.19 100.00 0.14
S15 0.02 0.00 0.00 0.17 0.25 0.16 0.12 0.06 0.00 0.00 0.00 0.00 0.06 0.14 100.00


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 2,696 4,720 7,946 62,423 62,423
Gene per cell 294 294 1,053 1,493 2,088 7,822 7,822
Mitochondrial (%) per cell 0.00 0.00 2.89 3.98 5.39 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] 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] spatstat.utils_3.0-4    rmarkdown_2.25         
## [101] RhpcBLASctl_0.23-42     XVector_0.42.0         
## [103] htmltools_0.5.7         pkgconfig_2.0.3        
## [105] highr_0.10              fastmap_1.1.1          
## [107] rlang_1.1.2             GlobalOptions_0.1.2    
## [109] htmlwidgets_1.6.4       shiny_1.8.0            
## [111] jquerylib_0.1.4         farver_2.1.1           
## [113] zoo_1.8-12              jsonlite_1.8.8         
## [115] mclust_6.0.1            R.oo_1.25.0            
## [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