Chapter 33 Mammary gland dataset

33.1 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.

33.2 Analysis code

33.2.5 Variance modelling

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

33.2.7 Clustering

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

Session Info

R version 3.6.1 (2019-07-05)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 14.04.5 LTS

Matrix products: default
BLAS:   /home/ramezqui/Rbuild/danbuild/R-3.6.1/lib/libRblas.so
LAPACK: /home/ramezqui/Rbuild/danbuild/R-3.6.1/lib/libRlapack.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] parallel  stats4    stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
 [1] BiocSingular_1.2.0          scran_1.14.3                ensembldb_2.10.0           
 [4] AnnotationFilter_1.10.0     GenomicFeatures_1.38.0      AnnotationDbi_1.48.0       
 [7] AnnotationHub_2.18.0        BiocFileCache_1.10.2        dbplyr_1.4.2               
[10] scater_1.14.3               ggplot2_3.2.1               scRNAseq_2.0.2             
[13] SingleCellExperiment_1.8.0  SummarizedExperiment_1.16.0 DelayedArray_0.12.0        
[16] BiocParallel_1.20.0         matrixStats_0.55.0          Biobase_2.46.0             
[19] GenomicRanges_1.38.0        GenomeInfoDb_1.22.0         IRanges_2.20.0             
[22] S4Vectors_0.24.0            BiocGenerics_0.32.0         Cairo_1.5-10               
[25] BiocStyle_2.14.0            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_0.26.0                BiocNeighbors_1.4.0           bit64_0.9-7                  
 [7] interactiveDisplayBase_1.24.0 codetools_0.2-16              knitr_1.26                   
[10] zeallot_0.1.0                 Rsamtools_2.2.0               shiny_1.4.0                  
[13] BiocManager_1.30.9            compiler_3.6.1                httr_1.4.1                   
[16] dqrng_0.2.1                   backports_1.1.5               assertthat_0.2.1             
[19] Matrix_1.2-17                 fastmap_1.0.1                 lazyeval_0.2.2               
[22] limma_3.42.0                  later_1.0.0                   htmltools_0.4.0              
[25] prettyunits_1.0.2             tools_3.6.1                   rsvd_1.0.2                   
[28] igraph_1.2.4.1                gtable_0.3.0                  glue_1.3.1                   
[31] GenomeInfoDbData_1.2.2        dplyr_0.8.3                   rappdirs_0.3.1               
[34] Rcpp_1.0.3                    vctrs_0.2.0                   Biostrings_2.54.0            
[37] ExperimentHub_1.12.0          rtracklayer_1.46.0            DelayedMatrixStats_1.8.0     
[40] xfun_0.11                     stringr_1.4.0                 mime_0.7                     
[43] irlba_2.3.3                   statmod_1.4.32                XML_3.98-1.20                
[46] edgeR_3.28.0                  zlibbioc_1.32.0               scales_1.0.0                 
[49] hms_0.5.2                     promises_1.1.0                ProtGenerics_1.18.0          
[52] yaml_2.2.0                    curl_4.2                      memoise_1.1.0                
[55] gridExtra_2.3                 biomaRt_2.42.0                stringi_1.4.3                
[58] RSQLite_2.1.2                 BiocVersion_3.10.1            rlang_0.4.1                  
[61] pkgconfig_2.0.3               bitops_1.0-6                  evaluate_0.14                
[64] lattice_0.20-38               purrr_0.3.3                   labeling_0.3                 
[67] GenomicAlignments_1.22.1      cowplot_1.0.0                 bit_1.1-14                   
[70] tidyselect_0.2.5              magrittr_1.5                  bookdown_0.15                
[73] R6_2.4.1                      DBI_1.0.0                     pillar_1.4.2                 
[76] withr_2.1.2                   RCurl_1.95-4.12               tibble_2.1.3                 
[79] crayon_1.3.4                  rmarkdown_1.17                viridis_0.5.1                
[82] progress_1.2.2                locfit_1.5-9.1                grid_3.6.1                   
[85] blob_1.2.0                    digest_0.6.22                 xtable_1.8-4                 
[88] httpuv_1.5.2                  openssl_1.4.1                 munsell_0.5.0                
[91] beeswarm_0.2.3                viridisLite_0.3.0             vipor_0.4.5                  
[94] 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.