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.
12.2 Data loading
12.3 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 12.1: 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 12.2: 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
## 14312.4 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 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.
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$trend(x), col='dodgerblue', add=TRUE, lwd=2)
Figure 12.4: Per-gene variance as a function of the mean for the log-expression values in the Bach mammary gland dataset. Each point represents a gene (black) with the mean-variance trend (blue) fitted to simulated Poisson counts.
12.6 Dimensionality reduction
library(BiocSingular)
set.seed(101010011)
sce.mam <- denoisePCA(sce.mam, technical=dec.mam, subset.row=top.mam)
sce.mam <- runTSNE(sce.mam, dimred="PCA")## [1] 1512.7 Clustering
We use a higher k to obtain coarser clusters (for use in doubletCluster() later).
snn.gr <- buildSNNGraph(sce.mam, use.dimred="PCA", k=25)
colLabels(sce.mam) <- factor(igraph::cluster_walktrap(snn.gr)$membership)##
## 1 2 3 4 5 6 7 8 9 10
## 550 799 716 452 24 84 52 39 32 24
Figure 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 4.1.0 (2021-05-18)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 20.04.2 LTS
Matrix products: default
BLAS: /home/biocbuild/bbs-3.13-bioc/R/lib/libRblas.so
LAPACK: /home/biocbuild/bbs-3.13-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_1.8.0 scran_1.20.0
[3] AnnotationHub_3.0.0 BiocFileCache_2.0.0
[5] dbplyr_2.1.1 scater_1.20.0
[7] ggplot2_3.3.3 scuttle_1.2.0
[9] ensembldb_2.16.0 AnnotationFilter_1.16.0
[11] GenomicFeatures_1.44.0 AnnotationDbi_1.54.0
[13] scRNAseq_2.6.0 SingleCellExperiment_1.14.0
[15] SummarizedExperiment_1.22.0 Biobase_2.52.0
[17] GenomicRanges_1.44.0 GenomeInfoDb_1.28.0
[19] IRanges_2.26.0 S4Vectors_0.30.0
[21] BiocGenerics_0.38.0 MatrixGenerics_1.4.0
[23] matrixStats_0.58.0 BiocStyle_2.20.0
[25] rebook_1.2.0
loaded via a namespace (and not attached):
[1] igraph_1.2.6 lazyeval_0.2.2
[3] BiocParallel_1.26.0 digest_0.6.27
[5] htmltools_0.5.1.1 viridis_0.6.1
[7] fansi_0.4.2 magrittr_2.0.1
[9] memoise_2.0.0 ScaledMatrix_1.0.0
[11] cluster_2.1.2 limma_3.48.0
[13] Biostrings_2.60.0 prettyunits_1.1.1
[15] colorspace_2.0-1 blob_1.2.1
[17] rappdirs_0.3.3 xfun_0.23
[19] dplyr_1.0.6 crayon_1.4.1
[21] RCurl_1.98-1.3 jsonlite_1.7.2
[23] graph_1.70.0 glue_1.4.2
[25] gtable_0.3.0 zlibbioc_1.38.0
[27] XVector_0.32.0 DelayedArray_0.18.0
[29] scales_1.1.1 edgeR_3.34.0
[31] DBI_1.1.1 Rcpp_1.0.6
[33] viridisLite_0.4.0 xtable_1.8-4
[35] progress_1.2.2 dqrng_0.3.0
[37] bit_4.0.4 rsvd_1.0.5
[39] metapod_1.0.0 httr_1.4.2
[41] dir.expiry_1.0.0 ellipsis_0.3.2
[43] pkgconfig_2.0.3 XML_3.99-0.6
[45] farver_2.1.0 CodeDepends_0.6.5
[47] sass_0.4.0 locfit_1.5-9.4
[49] utf8_1.2.1 tidyselect_1.1.1
[51] labeling_0.4.2 rlang_0.4.11
[53] later_1.2.0 munsell_0.5.0
[55] BiocVersion_3.13.1 tools_4.1.0
[57] cachem_1.0.5 generics_0.1.0
[59] RSQLite_2.2.7 ExperimentHub_2.0.0
[61] evaluate_0.14 stringr_1.4.0
[63] fastmap_1.1.0 yaml_2.2.1
[65] knitr_1.33 bit64_4.0.5
[67] purrr_0.3.4 KEGGREST_1.32.0
[69] sparseMatrixStats_1.4.0 mime_0.10
[71] biomaRt_2.48.0 compiler_4.1.0
[73] beeswarm_0.3.1 filelock_1.0.2
[75] curl_4.3.1 png_0.1-7
[77] interactiveDisplayBase_1.30.0 statmod_1.4.36
[79] tibble_3.1.2 bslib_0.2.5.1
[81] stringi_1.6.2 highr_0.9
[83] bluster_1.2.0 lattice_0.20-44
[85] ProtGenerics_1.24.0 Matrix_1.3-3
[87] vctrs_0.3.8 pillar_1.6.1
[89] lifecycle_1.0.0 BiocManager_1.30.15
[91] jquerylib_0.1.4 BiocNeighbors_1.10.0
[93] cowplot_1.1.1 bitops_1.0-7
[95] irlba_2.3.3 httpuv_1.6.1
[97] rtracklayer_1.52.0 R6_2.5.0
[99] BiocIO_1.2.0 bookdown_0.22
[101] promises_1.2.0.1 gridExtra_2.3
[103] vipor_0.4.5 codetools_0.2-18
[105] assertthat_0.2.1 rjson_0.2.20
[107] withr_2.4.2 GenomicAlignments_1.28.0
[109] Rsamtools_2.8.0 GenomeInfoDbData_1.2.6
[111] hms_1.1.0 grid_4.1.0
[113] beachmat_2.8.0 rmarkdown_2.8
[115] DelayedMatrixStats_1.14.0 Rtsne_0.15
[117] shiny_1.6.0 ggbeeswarm_0.6.0
[119] restfulr_0.0.13 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.