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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 lung sample_based none none 500 20 250 3 0.25 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
copd.o1 copd lung S1
copd.o2 copd lung S2
copd.o3 copd lung S3
ctl.o1 control lung S4
ctl.o2 control lung S5
ctl.o3 control lung S6
ctl.y1 control lung S7
ctl.y2 control lung S8
ctl.y3 control lung S9

3.1.2 Cells & Genes (RNA)

sample_id pre_qc_gene pre_qc_cell post_qc_gene post_qc_cell
S1 24,305 7,653 19,788 6,705
S2 24,473 6,941 20,230 6,201
S3 25,665 7,088 21,087 6,153
S4 22,824 6,347 18,503 5,228
S5 22,805 7,368 18,308 6,012
S6 24,943 8,268 20,630 7,133
S7 24,143 6,357 19,876 5,025
S8 25,453 8,229 21,004 7,338
S9 24,531 8,359 20,221 7,283

3.1.3 UMI per cell (RNA)

Vales are post-QC.

sample_id min 0% 25% 50% 75% 100% max
S1 503 503 2,258 3,726 6,061 37,537 37,537
S2 500 500 2,221 5,365 14,444 59,039 59,039
S3 501 501 1,937 3,123 8,678 81,770 81,770
S4 517 517 2,639 5,852 9,454 32,728 32,728
S5 500 500 2,362 4,902 8,031 26,993 26,993
S6 501 501 1,519 2,619 6,397 82,265 82,265
S7 500 500 2,613 4,569 8,241 44,545 44,545
S8 502 502 2,421 3,977 12,838 76,136 76,136
S9 501 501 2,159 8,042 14,097 39,383 39,383

3.1.4 Gene per cell (RNA)

Vales are post-QC.

sample_id min 0% 25% 50% 75% 100% max
S1 307 307 993 1,382 1,914 6,359 6,359
S2 306 306 1,070 1,966 3,586 6,991 6,991
S3 303 303 878 1,309 2,611 8,448 8,448
S4 303 303 1,159 1,984 2,706 5,307 5,307
S5 301 301 1,060 1,798 2,468 4,941 4,941
S6 301 301 780 1,188 2,085 9,526 9,526
S7 309 309 1,081 1,647 2,645 7,173 7,173
S8 313 313 1,151 1,715 3,472 7,506 7,506
S9 305 305 1,082 2,597 3,604 6,923 6,923

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 7.75 10.30 13.30 20.00 20.00
S2 0.00 0.00 6.62 8.77 11.51 20.00 20.00
S3 0.10 0.10 6.95 9.87 13.20 20.00 20.00
S4 0.22 0.22 7.10 9.82 13.28 19.96 19.96
S5 0.00 0.00 8.19 10.57 13.48 20.00 20.00
S6 0.00 0.00 7.06 9.99 13.43 20.00 20.00
S7 0.00 0.00 8.60 11.27 14.48 20.00 20.00
S8 0.00 0.00 6.33 9.02 11.94 19.98 19.98
S9 0.06 0.06 8.16 10.19 12.77 20.00 20.00


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.

## [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 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 2,122 4,288 9,324 82,265 82,265
Gene per cell 301 301 994 1,633 2,738 9,526 9,526
Mitochondrial (%) per cell 0.00 0.00 7.40 9.93 13.05 20.00 20.00

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


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