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.
40.2 Data loading
40.3 Quality control
is.mito <- rowData(sce.mam)$SEQNAME == "MT"
stats <- perCellQCMetrics(sce.mam, subsets=list(Mito=which(is.mito)))
qc <- quickPerCellQC(stats, percent_subsets="subsets_Mito_percent")
sce.mam <- sce.mam[,!qc$discard]colData(unfiltered) <- cbind(colData(unfiltered), stats)
unfiltered$discard <- qc$discard
gridExtra::grid.arrange(
plotColData(unfiltered, y="sum", colour_by="discard") +
scale_y_log10() + ggtitle("Total count"),
plotColData(unfiltered, y="detected", colour_by="discard") +
scale_y_log10() + ggtitle("Detected features"),
plotColData(unfiltered, y="subsets_Mito_percent",
colour_by="discard") + ggtitle("Mito percent"),
ncol=2
)
Figure 40.1: Distribution of each QC metric across cells in the Bach mammary gland dataset. Each point represents a cell and is colored according to whether that cell was discarded.
Figure 40.2: Percentage of mitochondrial reads in each cell in the Bach mammary gland dataset compared to its total count. Each point represents a cell and is colored according to whether that cell was discarded.
## low_lib_size low_n_features high_subsets_Mito_percent
## 0 0 143
## discard
## 14340.4 Normalization
library(scran)
set.seed(101000110)
clusters <- quickCluster(sce.mam)
sce.mam <- computeSumFactors(sce.mam, clusters=clusters)
sce.mam <- logNormCounts(sce.mam)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.271 0.522 0.758 1.000 1.204 10.958plot(librarySizeFactors(sce.mam), sizeFactors(sce.mam), pch=16,
xlab="Library size factors", ylab="Deconvolution factors", log="xy")
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.
set.seed(00010101)
dec.mam <- modelGeneVarByPoisson(sce.mam)
top.mam <- getTopHVGs(dec.mam, prop=0.1)plot(dec.mam$mean, dec.mam$total, pch=16, cex=0.5,
xlab="Mean of log-expression", ylab="Variance of log-expression")
curfit <- metadata(dec.mam)
curve(curfit$trend(x), col='dodgerblue', add=TRUE, lwd=2)
Figure 40.4: Per-gene variance as a function of the mean for the log-expression values in the Bach mammary gland dataset. Each point represents a gene (black) with the mean-variance trend (blue) fitted to simulated Poisson counts.
40.6 Dimensionality reduction
library(BiocSingular)
set.seed(101010011)
sce.mam <- denoisePCA(sce.mam, technical=dec.mam, subset.row=top.mam)
sce.mam <- runTSNE(sce.mam, dimred="PCA")## [1] 1540.7 Clustering
We use a higher k to obtain coarser clusters (for use in doubletCluster() later).
snn.gr <- buildSNNGraph(sce.mam, use.dimred="PCA", k=25)
colLabels(sce.mam) <- factor(igraph::cluster_walktrap(snn.gr)$membership)##
## 1 2 3 4 5 6 7 8 9 10
## 550 799 716 452 24 84 52 39 32 24
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 4.0.4 (2021-02-15)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 20.04.2 LTS
Matrix products: default
BLAS: /home/biocbuild/bbs-3.12-books/R/lib/libRblas.so
LAPACK: /home/biocbuild/bbs-3.12-books/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_1.18.5
[3] AnnotationHub_2.22.0 BiocFileCache_1.14.0
[5] dbplyr_2.1.0 scater_1.18.6
[7] ggplot2_3.3.3 ensembldb_2.14.0
[9] AnnotationFilter_1.14.0 GenomicFeatures_1.42.2
[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_1.26.4 IRanges_2.24.1
[19] S4Vectors_0.28.1 BiocGenerics_0.36.0
[21] MatrixGenerics_1.2.1 matrixStats_0.58.0
[23] BiocStyle_2.18.1 rebook_1.0.0
loaded via a namespace (and not attached):
[1] igraph_1.2.6 lazyeval_0.2.2
[3] BiocParallel_1.24.1 digest_0.6.27
[5] htmltools_0.5.1.1 viridis_0.5.1
[7] fansi_0.4.2 magrittr_2.0.1
[9] memoise_2.0.0 limma_3.46.0
[11] Biostrings_2.58.0 askpass_1.1
[13] prettyunits_1.1.1 colorspace_2.0-0
[15] blob_1.2.1 rappdirs_0.3.3
[17] xfun_0.22 dplyr_1.0.5
[19] callr_3.5.1 crayon_1.4.1
[21] RCurl_1.98-1.3 jsonlite_1.7.2
[23] graph_1.68.0 glue_1.4.2
[25] gtable_0.3.0 zlibbioc_1.36.0
[27] XVector_0.30.0 DelayedArray_0.16.2
[29] scales_1.1.1 edgeR_3.32.1
[31] DBI_1.1.1 Rcpp_1.0.6
[33] viridisLite_0.3.0 xtable_1.8-4
[35] progress_1.2.2 dqrng_0.2.1
[37] bit_4.0.4 rsvd_1.0.3
[39] httr_1.4.2 ellipsis_0.3.1
[41] pkgconfig_2.0.3 XML_3.99-0.6
[43] farver_2.1.0 scuttle_1.0.4
[45] CodeDepends_0.6.5 sass_0.3.1
[47] locfit_1.5-9.4 utf8_1.2.1
[49] tidyselect_1.1.0 labeling_0.4.2
[51] rlang_0.4.10 later_1.1.0.1
[53] munsell_0.5.0 BiocVersion_3.12.0
[55] tools_4.0.4 cachem_1.0.4
[57] generics_0.1.0 RSQLite_2.2.4
[59] ExperimentHub_1.16.0 evaluate_0.14
[61] stringr_1.4.0 fastmap_1.1.0
[63] yaml_2.2.1 processx_3.4.5
[65] knitr_1.31 bit64_4.0.5
[67] purrr_0.3.4 sparseMatrixStats_1.2.1
[69] mime_0.10 xml2_1.3.2
[71] biomaRt_2.46.3 compiler_4.0.4
[73] beeswarm_0.3.1 curl_4.3
[75] interactiveDisplayBase_1.28.0 statmod_1.4.35
[77] tibble_3.1.0 bslib_0.2.4
[79] stringi_1.5.3 highr_0.8
[81] ps_1.6.0 lattice_0.20-41
[83] bluster_1.0.0 ProtGenerics_1.22.0
[85] Matrix_1.3-2 vctrs_0.3.6
[87] pillar_1.5.1 lifecycle_1.0.0
[89] BiocManager_1.30.10 jquerylib_0.1.3
[91] BiocNeighbors_1.8.2 cowplot_1.1.1
[93] bitops_1.0-6 irlba_2.3.3
[95] httpuv_1.5.5 rtracklayer_1.50.0
[97] R6_2.5.0 bookdown_0.21
[99] promises_1.2.0.1 gridExtra_2.3
[101] vipor_0.4.5 codetools_0.2-18
[103] assertthat_0.2.1 openssl_1.4.3
[105] withr_2.4.1 GenomicAlignments_1.26.0
[107] Rsamtools_2.6.0 GenomeInfoDbData_1.2.4
[109] hms_1.0.0 grid_4.0.4
[111] beachmat_2.6.4 rmarkdown_2.7
[113] DelayedMatrixStats_1.12.3 Rtsne_0.15
[115] shiny_1.6.0 ggbeeswarm_0.6.0 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.