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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 cell_based none none 750 10 250 3 0 LogNormalize 1e+06 2000 30 20 0.7 none 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 sample_id sample_name visit disease tissue subject
GSE116222 S1 A1,A2,A3,B1,B2,B3,C1,C2,C3 1,2,3 Healthy,UC_Non_Inflamed,UC_Inflamed Distal_Colon 3_Controls_Pool,3_UC_Pool

3.1.2 Cells & Genes (RNA)

sample_id pre_qc_gene pre_qc_cell post_qc_gene post_qc_cell
S1 28,003 11,175 22,607 11,157

3.1.3 UMI per cell (RNA)

Vales are post-QC.

sample_id min 0% 25% 50% 75% 100% max
S1 756 756 1,837 2,134 2,490 3,805 3,805

3.1.4 Gene per cell (RNA)

Vales are post-QC.

sample_id min 0% 25% 50% 75% 100% max
S1 358 358 1,065 1,472 2,216 5,753 5,753

3.1.5 Mitochondrial (%) per cell (RNA)

Vales are post-QC.

sample_id min 0% 25% 50% 75% 100% max
S1 0.03 0.03 1.95 2.44 3.05 8.68 8.68


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
S1 11157

3.3.2 Jaccard Index

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

S1
S1 1


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 756 756 1,837 2,134 2,490 3,805 3,805
Gene per cell 358 358 1,065 1,472 2,216 5,753 5,753
Mitochondrial (%) per cell 0.03 0.03 1.95 2.44 3.05 8.68 8.68

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


3.7 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 Mitochondrial (%) per cell - SPRING Projection

3.7.4 UMI per cell - UMAP Projection

3.7.5 Gene per cell - UMAP Projection

3.7.6 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_id

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                lazyeval_0.2.2         
##  [11] uwot_0.1.16             ggdist_3.3.1           
##  [13] GetoptLong_1.0.5        withr_2.5.2            
##  [15] progressr_0.14.0        cli_3.6.1              
##  [17] spatstat.explore_3.2-5  fastDummies_1.7.3      
##  [19] labeling_0.4.3          sass_0.4.8             
##  [21] mvtnorm_1.2-4           spatstat.data_3.0-3    
##  [23] proxy_0.4-27            ggridges_0.5.4         
##  [25] pbapply_1.7-2           commonmark_1.9.0       
##  [27] systemfonts_1.0.5       R.utils_2.12.3         
##  [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             R.methodsS3_1.8.2      
##  [43] lifecycle_1.0.4         yaml_2.3.7             
##  [45] SparseArray_1.2.2       Rtsne_0.17             
##  [47] promises_1.2.1          crayon_1.5.2           
##  [49] miniUI_0.1.1.1          lattice_0.22-5         
##  [51] pillar_1.9.0            knitr_1.45             
##  [53] rjson_0.2.21            boot_1.3-28.1          
##  [55] gld_2.6.6               future.apply_1.11.0    
##  [57] codetools_0.2-19        leiden_0.4.3.1         
##  [59] glue_1.6.2              vctrs_0.6.5            
##  [61] png_0.1-8               spam_2.10-0            
##  [63] neuralnet_1.44.2        cellranger_1.1.0       
##  [65] gtable_0.3.4            cachem_1.0.8           
##  [67] ks_1.14.1               xfun_0.41              
##  [69] S4Arrays_1.2.0          mime_0.12              
##  [71] pracma_2.4.4            survival_3.5-7         
##  [73] pbmcapply_1.5.1         iterators_1.0.14       
##  [75] statmod_1.5.0           ellipsis_0.3.2         
##  [77] fitdistrplus_1.1-11     ROCR_1.0-11            
##  [79] nlme_3.1-164            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] compiler_4.3.2          rvest_1.0.3            
##  [89] expm_0.999-8            xml2_1.3.6             
##  [91] DelayedArray_0.28.0     scales_1.3.0           
##  [93] caTools_1.18.2          lmtest_0.9-40          
##  [95] stringr_1.5.1           digest_0.6.33          
##  [97] goftest_1.2-3           spatstat.utils_3.0-4   
##  [99] rmarkdown_2.25          XVector_0.42.0         
## [101] htmltools_0.5.7         pkgconfig_2.0.3        
## [103] highr_0.10              fastmap_1.1.1          
## [105] rlang_1.1.2             GlobalOptions_0.1.2    
## [107] htmlwidgets_1.6.4       shiny_1.8.0            
## [109] jquerylib_0.1.4         farver_2.1.1           
## [111] zoo_1.8-12              jsonlite_1.8.8         
## [113] mclust_6.0.1            R.oo_1.25.0            
## [115] RCurl_1.98-1.13         magrittr_2.0.3         
## [117] GenomeInfoDbData_1.2.11 dotCall64_1.1-1        
## [119] munsell_0.5.0           stringi_1.8.2          
## [121] rootSolve_1.8.2.4       zlibbioc_1.48.0        
## [123] MASS_7.3-60             plyr_1.8.9             
## [125] parallel_4.3.2          listenv_0.9.0          
## [127] lmom_3.0                deldir_2.0-2           
## [129] splines_4.3.2           tensor_1.5             
## [131] circlize_0.4.15         locfit_1.5-9.8         
## [133] spatstat.geom_3.2-7     markdown_1.12          
## [135] RcppHNSW_0.5.0          reshape2_1.4.4         
## [137] evaluate_0.23           foreach_1.5.2          
## [139] httpuv_1.6.13           RANN_2.6.1             
## [141] tidyr_1.3.0             getopt_1.20.4          
## [143] polyclip_1.10-6         future_1.33.0          
## [145] clue_0.3-65             scattermore_1.2        
## [147] xtable_1.8-4            e1071_1.7-14           
## [149] RSpectra_0.16-1         later_1.3.2            
## [151] viridisLite_0.4.2       class_7.3-22           
## [153] tibble_3.2.1            cluster_2.1.6          
## [155] globals_0.16.2