Chapter 12 Bach mouse mammary gland (10X Genomics)

12.1 Introduction

This performs an analysis of the Bach et al. (2017) 10X Genomics dataset, from which we will consider a single sample of epithelial cells from the mouse mammary gland during gestation.

12.4 Normalization

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   0.271   0.522   0.758   1.000   1.204  10.958
Relationship between the library size factors and the deconvolution size factors in the Bach mammary gland dataset.

Figure 12.3: Relationship between the library size factors and the deconvolution size factors in the Bach mammary gland dataset.

12.5 Variance modelling

We use a Poisson-based technical trend to capture more genuine biological variation in the biological component.

Per-gene variance as a function of the mean for the log-expression values in the Bach mammary gland dataset. Each point represents a gene (black) with the mean-variance trend (blue) fitted to simulated Poisson counts.

Figure 12.4: Per-gene variance as a function of the mean for the log-expression values in the Bach mammary gland dataset. Each point represents a gene (black) with the mean-variance trend (blue) fitted to simulated Poisson counts.

12.7 Clustering

We use a higher k to obtain coarser clusters (for use in doubletCluster() later).

## 
##   1   2   3   4   5   6   7   8   9  10 
## 550 799 716 452  24  84  52  39  32  24
Obligatory $t$-SNE plot of the Bach mammary gland dataset, where each point represents a cell and is colored according to the assigned cluster.

Figure 12.5: Obligatory \(t\)-SNE plot of the Bach mammary gland dataset, where each point represents a cell and is colored according to the assigned cluster.

Session Info

R version 4.2.1 (2022-06-23)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 20.04.5 LTS

Matrix products: default
BLAS:   /home/biocbuild/bbs-3.15-bioc/R/lib/libRblas.so
LAPACK: /home/biocbuild/bbs-3.15-bioc/R/lib/libRlapack.so

locale:
 [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
 [3] LC_TIME=en_GB              LC_COLLATE=C              
 [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
 [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
 [9] LC_ADDRESS=C               LC_TELEPHONE=C            
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       

attached base packages:
[1] stats4    stats     graphics  grDevices utils     datasets  methods  
[8] base     

other attached packages:
 [1] BiocSingular_1.12.0         scran_1.24.1               
 [3] AnnotationHub_3.4.0         BiocFileCache_2.4.0        
 [5] dbplyr_2.2.1                scater_1.24.0              
 [7] ggplot2_3.3.6               scuttle_1.6.3              
 [9] ensembldb_2.20.2            AnnotationFilter_1.20.0    
[11] GenomicFeatures_1.48.4      AnnotationDbi_1.58.0       
[13] scRNAseq_2.10.0             SingleCellExperiment_1.18.1
[15] SummarizedExperiment_1.26.1 Biobase_2.56.0             
[17] GenomicRanges_1.48.0        GenomeInfoDb_1.32.4        
[19] IRanges_2.30.1              S4Vectors_0.34.0           
[21] BiocGenerics_0.42.0         MatrixGenerics_1.8.1       
[23] matrixStats_0.62.0          BiocStyle_2.24.0           
[25] rebook_1.6.0               

loaded via a namespace (and not attached):
  [1] igraph_1.3.5                  lazyeval_0.2.2               
  [3] BiocParallel_1.30.4           digest_0.6.29                
  [5] htmltools_0.5.3               viridis_0.6.2                
  [7] fansi_1.0.3                   magrittr_2.0.3               
  [9] memoise_2.0.1                 ScaledMatrix_1.4.1           
 [11] cluster_2.1.4                 limma_3.52.4                 
 [13] Biostrings_2.64.1             prettyunits_1.1.1            
 [15] colorspace_2.0-3              blob_1.2.3                   
 [17] rappdirs_0.3.3                ggrepel_0.9.1                
 [19] xfun_0.33                     dplyr_1.0.10                 
 [21] crayon_1.5.2                  RCurl_1.98-1.9               
 [23] jsonlite_1.8.2                graph_1.74.0                 
 [25] glue_1.6.2                    gtable_0.3.1                 
 [27] zlibbioc_1.42.0               XVector_0.36.0               
 [29] DelayedArray_0.22.0           scales_1.2.1                 
 [31] edgeR_3.38.4                  DBI_1.1.3                    
 [33] Rcpp_1.0.9                    viridisLite_0.4.1            
 [35] xtable_1.8-4                  progress_1.2.2               
 [37] dqrng_0.3.0                   bit_4.0.4                    
 [39] rsvd_1.0.5                    metapod_1.4.0                
 [41] httr_1.4.4                    dir.expiry_1.4.0             
 [43] ellipsis_0.3.2                pkgconfig_2.0.3              
 [45] XML_3.99-0.11                 farver_2.1.1                 
 [47] CodeDepends_0.6.5             sass_0.4.2                   
 [49] locfit_1.5-9.6                utf8_1.2.2                   
 [51] labeling_0.4.2                tidyselect_1.2.0             
 [53] rlang_1.0.6                   later_1.3.0                  
 [55] munsell_0.5.0                 BiocVersion_3.15.2           
 [57] tools_4.2.1                   cachem_1.0.6                 
 [59] cli_3.4.1                     generics_0.1.3               
 [61] RSQLite_2.2.18                ExperimentHub_2.4.0          
 [63] evaluate_0.17                 stringr_1.4.1                
 [65] fastmap_1.1.0                 yaml_2.3.5                   
 [67] knitr_1.40                    bit64_4.0.5                  
 [69] purrr_0.3.5                   KEGGREST_1.36.3              
 [71] sparseMatrixStats_1.8.0       mime_0.12                    
 [73] xml2_1.3.3                    biomaRt_2.52.0               
 [75] compiler_4.2.1                beeswarm_0.4.0               
 [77] filelock_1.0.2                curl_4.3.3                   
 [79] png_0.1-7                     interactiveDisplayBase_1.34.0
 [81] statmod_1.4.37                tibble_3.1.8                 
 [83] bslib_0.4.0                   stringi_1.7.8                
 [85] highr_0.9                     bluster_1.6.0                
 [87] lattice_0.20-45               ProtGenerics_1.28.0          
 [89] Matrix_1.5-1                  vctrs_0.4.2                  
 [91] pillar_1.8.1                  lifecycle_1.0.3              
 [93] BiocManager_1.30.18           jquerylib_0.1.4              
 [95] BiocNeighbors_1.14.0          cowplot_1.1.1                
 [97] bitops_1.0-7                  irlba_2.3.5.1                
 [99] httpuv_1.6.6                  rtracklayer_1.56.1           
[101] R6_2.5.1                      BiocIO_1.6.0                 
[103] bookdown_0.29                 promises_1.2.0.1             
[105] gridExtra_2.3                 vipor_0.4.5                  
[107] codetools_0.2-18              assertthat_0.2.1             
[109] rjson_0.2.21                  withr_2.5.0                  
[111] GenomicAlignments_1.32.1      Rsamtools_2.12.0             
[113] GenomeInfoDbData_1.2.8        parallel_4.2.1               
[115] hms_1.1.2                     grid_4.2.1                   
[117] beachmat_2.12.0               rmarkdown_2.17               
[119] DelayedMatrixStats_1.18.2     Rtsne_0.16                   
[121] shiny_1.7.2                   ggbeeswarm_0.6.0             
[123] restfulr_0.0.15

References

Bach, K., S. Pensa, M. Grzelak, J. Hadfield, D. J. Adams, J. C. Marioni, and W. T. Khaled. 2017. “Differentiation dynamics of mammary epithelial cells revealed by single-cell RNA sequencing.” Nat Commun 8 (1): 2128.