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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 colon sample_based none none 500 12 200 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
UC-control-rep1 UC_uninflamed Colon S1
UC-control-rep2 UC_uninflamed Colon S2
UC-control-rep3 UC_uninflamed Colon S3
UC-control-rep4 UC_uninflamed Colon S4
UC-rep1 UC_inflamed Colon S5
UC-rep2 UC_inflamed Colon S6
UC-rep3 UC_inflamed Colon S7
UC-rep4 UC_inflamed Colon S8
HC-rep5 Control Colon S9
HC-rep6 Control Colon S10
HC-rep7 Control Colon S11
HC-rep8 Control Colon S12

3.1.2 Cells & Genes (RNA)

sample_id pre_qc_gene pre_qc_cell post_qc_gene post_qc_cell
S1 22,939 4,890 16,918 2,124
S2 23,264 6,437 18,074 4,373
S3 22,555 4,352 17,063 1,774
S4 23,722 7,233 18,578 4,282
S5 19,259 811 14,237 622
S6 22,592 7,561 18,052 5,711
S7 22,693 5,654 18,169 3,286
S8 22,884 5,833 18,291 4,188
S9 21,725 5,701 16,208 3,844
S10 22,385 4,998 16,774 2,889
S11 23,768 7,096 18,377 3,658
S12 23,765 6,822 18,862 3,960

3.1.3 UMI per cell (RNA)

Vales are post-QC.

sample_id min 0% 25% 50% 75% 100% max
S1 500 500 1,061 2,277 3,266 73,517 73,517
S2 500 500 894 2,195 3,478 101,639 101,639
S3 500 500 1,646 2,818 5,258 51,490 51,490
S4 500 500 1,648 3,032 4,869 47,381 47,381
S5 500 500 671 1,315 4,236 94,043 94,043
S6 500 500 1,158 2,214 3,328 78,878 78,878
S7 500 500 1,970 3,412 12,804 78,983 78,983
S8 501 501 2,128 3,332 6,066 51,891 51,891
S9 500 500 1,213 1,649 2,440 28,175 28,175
S10 500 500 1,544 2,584 5,374 43,248 43,248
S11 500 500 1,023 2,808 5,332 75,874 75,874
S12 501 501 1,628 3,588 11,078 54,681 54,681

3.1.4 Gene per cell (RNA)

Vales are post-QC.

sample_id min 0% 25% 50% 75% 100% max
S1 200 200 455 832 1,105 6,591 6,591
S2 200 200 388 813 1,154 8,681 8,681
S3 200 200 590 1,016 1,372 6,619 6,619
S4 201 201 487 1,032 1,387 7,000 7,000
S5 200 200 354 546 1,272 8,362 8,362
S6 200 200 356 741 1,082 7,190 7,190
S7 200 200 481 981 1,540 7,875 7,875
S8 200 200 562 1,074 1,474 7,743 7,743
S9 200 200 484 594 743 4,501 4,501
S10 200 200 523 775 1,027 6,169 6,169
S11 200 200 418 926 1,371 7,715 7,715
S12 200 200 575 1,142 1,719 7,613 7,613

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 3.09 4.49 6.85 11.99 11.99
S2 0.09 0.09 2.19 3.68 5.40 11.98 11.98
S3 0.11 0.11 2.43 4.51 6.68 11.97 11.97
S4 0.00 0.00 1.36 3.05 4.75 11.99 11.99
S5 0.10 0.10 1.63 3.08 4.48 11.94 11.94
S6 0.00 0.00 0.80 2.56 4.21 11.99 11.99
S7 0.00 0.00 0.92 2.34 4.36 11.99 11.99
S8 0.00 0.00 0.82 2.29 3.57 11.96 11.96
S9 0.07 0.07 2.74 4.12 5.54 11.97 11.97
S10 0.00 0.00 2.12 4.60 6.60 12.00 12.00
S11 0.07 0.07 1.68 3.21 5.22 11.99 11.99
S12 0.00 0.00 1.48 3.29 5.11 11.99 11.99


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
S1 2124 9 5 17 25 16 15 9 13 8 10 11
S2 9 4373 11 30 2 39 19 20 20 16 21 20
S3 5 11 1774 9 0 11 10 8 7 4 6 9
S4 17 30 9 4282 5 40 18 34 21 13 24 22
S5 25 2 0 5 622 7 3 4 1 6 5 6
S6 16 39 11 40 7 5711 28 29 38 16 22 32
S7 15 19 10 18 3 28 3286 16 16 15 15 15
S8 9 20 8 34 4 29 16 4188 22 12 21 20
S9 13 20 7 21 1 38 16 22 3844 11 13 20
S10 8 16 4 13 6 16 15 12 11 2889 16 17
S11 10 21 6 24 5 22 15 21 13 16 3658 23
S12 11 20 9 22 6 32 15 20 20 17 23 3960

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
S1 100.00 0.28 0.26 0.53 1.82 0.41 0.55 0.29 0.44 0.32 0.35 0.36
S2 0.28 100.00 0.36 0.69 0.08 0.77 0.50 0.47 0.49 0.44 0.52 0.48
S3 0.26 0.36 100.00 0.30 0.00 0.29 0.40 0.27 0.25 0.17 0.22 0.31
S4 0.53 0.69 0.30 100.00 0.20 0.80 0.48 0.80 0.52 0.36 0.60 0.53
S5 1.82 0.08 0.00 0.20 100.00 0.22 0.15 0.17 0.04 0.34 0.23 0.26
S6 0.41 0.77 0.29 0.80 0.22 100.00 0.62 0.59 0.80 0.37 0.47 0.66
S7 0.55 0.50 0.40 0.48 0.15 0.62 100.00 0.43 0.45 0.49 0.43 0.41
S8 0.29 0.47 0.27 0.80 0.17 0.59 0.43 100.00 0.55 0.34 0.54 0.49
S9 0.44 0.49 0.25 0.52 0.04 0.80 0.45 0.55 100.00 0.33 0.35 0.51
S10 0.32 0.44 0.17 0.36 0.34 0.37 0.49 0.34 0.33 100.00 0.49 0.50
S11 0.35 0.52 0.22 0.60 0.23 0.47 0.43 0.54 0.35 0.49 100.00 0.60
S12 0.36 0.48 0.31 0.53 0.26 0.66 0.41 0.49 0.51 0.50 0.60 100.00


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 500 500 1,293 2,605 4,636 101,639 101,639
Gene per cell 200 200 450 860 1,261 8,681 8,681
Mitochondrial (%) per cell 0.00 0.00 1.50 3.28 5.11 12.00 12.00

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