Chapter 36 Bach mouse mammary gland (10X Genomics)

36.1 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.

36.4 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 36.3: Relationship between the library size factors and the deconvolution size factors in the Bach mammary gland dataset.

36.5 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 36.4: 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.

36.7 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 36.5: 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 Under development (unstable) (2019-12-29 r77627)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 14.04.6 LTS

Matrix products: default
BLAS/LAPACK: /app/easybuild/software/OpenBLAS/0.2.18-GCC-5.4.0-2.26-LAPACK-3.6.1/lib/libopenblas_prescottp-r0.2.18.so

locale:
 [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C               LC_TIME=en_US.UTF-8       
 [4] LC_COLLATE=C               LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
 [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                  LC_ADDRESS=C              
[10] LC_TELEPHONE=C             LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       

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

other attached packages:
 [1] BiocSingular_1.3.1          scran_1.15.14               ensembldb_2.11.2           
 [4] AnnotationFilter_1.11.0     GenomicFeatures_1.39.2      AnnotationDbi_1.49.0       
 [7] AnnotationHub_2.19.3        BiocFileCache_1.11.4        dbplyr_1.4.2               
[10] scater_1.15.12              ggplot2_3.2.1               scRNAseq_2.1.5             
[13] SingleCellExperiment_1.9.1  SummarizedExperiment_1.17.1 DelayedArray_0.13.2        
[16] BiocParallel_1.21.2         matrixStats_0.55.0          Biobase_2.47.2             
[19] GenomicRanges_1.39.1        GenomeInfoDb_1.23.1         IRanges_2.21.2             
[22] S4Vectors_0.25.8            BiocGenerics_0.33.0         Cairo_1.5-10               
[25] BiocStyle_2.15.3            OSCAUtils_0.0.1            

loaded via a namespace (and not attached):
  [1] Rtsne_0.15                    ggbeeswarm_0.6.0              colorspace_1.4-1             
  [4] XVector_0.27.0                BiocNeighbors_1.5.1           farver_2.0.1                 
  [7] bit64_0.9-7                   interactiveDisplayBase_1.25.0 codetools_0.2-16             
 [10] knitr_1.26                    zeallot_0.1.0                 Rsamtools_2.3.2              
 [13] shiny_1.4.0                   BiocManager_1.30.10           compiler_4.0.0               
 [16] httr_1.4.1                    dqrng_0.2.1                   backports_1.1.5              
 [19] assertthat_0.2.1              Matrix_1.2-18                 fastmap_1.0.1                
 [22] lazyeval_0.2.2                limma_3.43.0                  later_1.0.0                  
 [25] htmltools_0.4.0               prettyunits_1.0.2             tools_4.0.0                  
 [28] igraph_1.2.4.2                rsvd_1.0.2                    gtable_0.3.0                 
 [31] glue_1.3.1                    GenomeInfoDbData_1.2.2        dplyr_0.8.3                  
 [34] rappdirs_0.3.1                Rcpp_1.0.3                    vctrs_0.2.1                  
 [37] Biostrings_2.55.4             ExperimentHub_1.13.5          rtracklayer_1.47.0           
 [40] DelayedMatrixStats_1.9.0      xfun_0.11                     stringr_1.4.0                
 [43] ps_1.3.0                      mime_0.8                      lifecycle_0.1.0              
 [46] irlba_2.3.3                   statmod_1.4.32                XML_3.98-1.20                
 [49] edgeR_3.29.0                  zlibbioc_1.33.0               scales_1.1.0                 
 [52] hms_0.5.2                     promises_1.1.0                ProtGenerics_1.19.3          
 [55] yaml_2.2.0                    curl_4.3                      memoise_1.1.0                
 [58] gridExtra_2.3                 biomaRt_2.43.0                stringi_1.4.3                
 [61] RSQLite_2.2.0                 highr_0.8                     BiocVersion_3.11.1           
 [64] rlang_0.4.2                   pkgconfig_2.0.3               bitops_1.0-6                 
 [67] evaluate_0.14                 lattice_0.20-38               purrr_0.3.3                  
 [70] labeling_0.3                  GenomicAlignments_1.23.1      cowplot_1.0.0                
 [73] bit_1.1-14                    processx_3.4.1                tidyselect_0.2.5             
 [76] magrittr_1.5                  bookdown_0.16                 R6_2.4.1                     
 [79] DBI_1.1.0                     pillar_1.4.3                  withr_2.1.2                  
 [82] RCurl_1.95-4.12               tibble_2.1.3                  crayon_1.3.4                 
 [85] rmarkdown_2.0                 viridis_0.5.1                 progress_1.2.2               
 [88] locfit_1.5-9.1                grid_4.0.0                    blob_1.2.0                   
 [91] callr_3.4.0                   digest_0.6.23                 xtable_1.8-4                 
 [94] httpuv_1.5.2                  openssl_1.4.1                 munsell_0.5.0                
 [97] beeswarm_0.2.3                viridisLite_0.3.0             vipor_0.4.5                  
[100] 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.