Chapter 40 Bach mouse mammary gland (10X Genomics)

40.1 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.

40.4 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 40.3: Relationship between the library size factors and the deconvolution size factors in the Bach mammary gland dataset.

40.5 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 40.4: 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.

40.7 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 40.5: 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 4.0.3 (2020-10-10)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 20.04.1 LTS

Matrix products: default
BLAS:   /home/biocbuild/bbs-3.12-bioc/R/lib/libRblas.so
LAPACK: /home/biocbuild/bbs-3.12-bioc/R/lib/libRlapack.so

locale:
 [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
 [3] LC_TIME=en_US.UTF-8        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] parallel  stats4    stats     graphics  grDevices utils     datasets 
[8] methods   base     

other attached packages:
 [1] BiocSingular_1.6.0          scran_1.18.0               
 [3] AnnotationHub_2.22.0        BiocFileCache_1.14.0       
 [5] dbplyr_1.4.4                scater_1.18.0              
 [7] ggplot2_3.3.2               ensembldb_2.14.0           
 [9] AnnotationFilter_1.14.0     GenomicFeatures_1.42.0     
[11] AnnotationDbi_1.52.0        scRNAseq_2.4.0             
[13] SingleCellExperiment_1.12.0 SummarizedExperiment_1.20.0
[15] Biobase_2.50.0              GenomicRanges_1.42.0       
[17] GenomeInfoDb_1.26.0         IRanges_2.24.0             
[19] S4Vectors_0.28.0            BiocGenerics_0.36.0        
[21] MatrixGenerics_1.2.0        matrixStats_0.57.0         
[23] BiocStyle_2.18.0            rebook_1.0.0               

loaded via a namespace (and not attached):
  [1] Rtsne_0.15                    ggbeeswarm_0.6.0             
  [3] colorspace_1.4-1              ellipsis_0.3.1               
  [5] scuttle_1.0.0                 bluster_1.0.0                
  [7] XVector_0.30.0                BiocNeighbors_1.8.0          
  [9] farver_2.0.3                  bit64_4.0.5                  
 [11] interactiveDisplayBase_1.28.0 xml2_1.3.2                   
 [13] codetools_0.2-16              sparseMatrixStats_1.2.0      
 [15] knitr_1.30                    Rsamtools_2.6.0              
 [17] graph_1.68.0                  shiny_1.5.0                  
 [19] BiocManager_1.30.10           compiler_4.0.3               
 [21] httr_1.4.2                    dqrng_0.2.1                  
 [23] assertthat_0.2.1              Matrix_1.2-18                
 [25] fastmap_1.0.1                 lazyeval_0.2.2               
 [27] limma_3.46.0                  later_1.1.0.1                
 [29] htmltools_0.5.0               prettyunits_1.1.1            
 [31] tools_4.0.3                   igraph_1.2.6                 
 [33] rsvd_1.0.3                    gtable_0.3.0                 
 [35] glue_1.4.2                    GenomeInfoDbData_1.2.4       
 [37] dplyr_1.0.2                   rappdirs_0.3.1               
 [39] Rcpp_1.0.5                    vctrs_0.3.4                  
 [41] Biostrings_2.58.0             ExperimentHub_1.16.0         
 [43] rtracklayer_1.50.0            DelayedMatrixStats_1.12.0    
 [45] xfun_0.19                     stringr_1.4.0                
 [47] ps_1.4.0                      beachmat_2.6.0               
 [49] mime_0.9                      lifecycle_0.2.0              
 [51] irlba_2.3.3                   statmod_1.4.35               
 [53] XML_3.99-0.5                  edgeR_3.32.0                 
 [55] zlibbioc_1.36.0               scales_1.1.1                 
 [57] hms_0.5.3                     promises_1.1.1               
 [59] ProtGenerics_1.22.0           yaml_2.2.1                   
 [61] curl_4.3                      gridExtra_2.3                
 [63] memoise_1.1.0                 biomaRt_2.46.0               
 [65] stringi_1.5.3                 RSQLite_2.2.1                
 [67] highr_0.8                     BiocVersion_3.12.0           
 [69] BiocParallel_1.24.0           rlang_0.4.8                  
 [71] pkgconfig_2.0.3               bitops_1.0-6                 
 [73] evaluate_0.14                 lattice_0.20-41              
 [75] purrr_0.3.4                   labeling_0.4.2               
 [77] GenomicAlignments_1.26.0      CodeDepends_0.6.5            
 [79] cowplot_1.1.0                 bit_4.0.4                    
 [81] processx_3.4.4                tidyselect_1.1.0             
 [83] magrittr_1.5                  bookdown_0.21                
 [85] R6_2.5.0                      generics_0.1.0               
 [87] DelayedArray_0.16.0           DBI_1.1.0                    
 [89] pillar_1.4.6                  withr_2.3.0                  
 [91] RCurl_1.98-1.2                tibble_3.0.4                 
 [93] crayon_1.3.4                  rmarkdown_2.5                
 [95] viridis_0.5.1                 progress_1.2.2               
 [97] locfit_1.5-9.4                grid_4.0.3                   
 [99] blob_1.2.1                    callr_3.5.1                  
[101] digest_0.6.27                 xtable_1.8-4                 
[103] httpuv_1.5.4                  openssl_1.4.3                
[105] munsell_0.5.0                 viridisLite_0.3.0            
[107] beeswarm_0.2.3                vipor_0.4.5                  
[109] askpass_1.1                  

Bibliography

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.