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.
12.3 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 4.3.0 RC (2023-04-13 r84269)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 22.04.2 LTS
Matrix products: default
BLAS: /home/biocbuild/bbs-3.17-bioc/R/lib/libRblas.so
LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.10.0
locale:
[1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
[3] LC_TIME=en_GB 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
time zone: America/New_York
tzcode source: system (glibc)
attached base packages:
[1] stats4 stats graphics grDevices utils datasets methods
[8] base
other attached packages:
[1] BiocSingular_1.16.0 scran_1.28.0
[3] AnnotationHub_3.8.0 BiocFileCache_2.8.0
[5] dbplyr_2.3.2 scater_1.28.0
[7] ggplot2_3.4.2 scuttle_1.10.0
[9] ensembldb_2.24.0 AnnotationFilter_1.24.0
[11] GenomicFeatures_1.52.0 AnnotationDbi_1.62.0
[13] scRNAseq_2.13.0 SingleCellExperiment_1.22.0
[15] SummarizedExperiment_1.30.0 Biobase_2.60.0
[17] GenomicRanges_1.52.0 GenomeInfoDb_1.36.0
[19] IRanges_2.34.0 S4Vectors_0.38.0
[21] BiocGenerics_0.46.0 MatrixGenerics_1.12.0
[23] matrixStats_0.63.0 BiocStyle_2.28.0
[25] rebook_1.10.0
loaded via a namespace (and not attached):
[1] jsonlite_1.8.4 CodeDepends_0.6.5
[3] magrittr_2.0.3 ggbeeswarm_0.7.1
[5] farver_2.1.1 rmarkdown_2.21
[7] BiocIO_1.10.0 zlibbioc_1.46.0
[9] vctrs_0.6.2 memoise_2.0.1
[11] Rsamtools_2.16.0 DelayedMatrixStats_1.22.0
[13] RCurl_1.98-1.12 htmltools_0.5.5
[15] progress_1.2.2 curl_5.0.0
[17] BiocNeighbors_1.18.0 sass_0.4.5
[19] bslib_0.4.2 cachem_1.0.7
[21] GenomicAlignments_1.36.0 igraph_1.4.2
[23] mime_0.12 lifecycle_1.0.3
[25] pkgconfig_2.0.3 rsvd_1.0.5
[27] Matrix_1.5-4 R6_2.5.1
[29] fastmap_1.1.1 GenomeInfoDbData_1.2.10
[31] shiny_1.7.4 digest_0.6.31
[33] colorspace_2.1-0 dqrng_0.3.0
[35] irlba_2.3.5.1 ExperimentHub_2.8.0
[37] RSQLite_2.3.1 beachmat_2.16.0
[39] labeling_0.4.2 filelock_1.0.2
[41] fansi_1.0.4 httr_1.4.5
[43] compiler_4.3.0 bit64_4.0.5
[45] withr_2.5.0 BiocParallel_1.34.0
[47] viridis_0.6.2 DBI_1.1.3
[49] highr_0.10 biomaRt_2.56.0
[51] rappdirs_0.3.3 DelayedArray_0.26.0
[53] bluster_1.10.0 rjson_0.2.21
[55] tools_4.3.0 vipor_0.4.5
[57] beeswarm_0.4.0 interactiveDisplayBase_1.38.0
[59] httpuv_1.6.9 glue_1.6.2
[61] restfulr_0.0.15 promises_1.2.0.1
[63] grid_4.3.0 Rtsne_0.16
[65] cluster_2.1.4 generics_0.1.3
[67] gtable_0.3.3 hms_1.1.3
[69] metapod_1.8.0 ScaledMatrix_1.8.0
[71] xml2_1.3.3 utf8_1.2.3
[73] XVector_0.40.0 ggrepel_0.9.3
[75] BiocVersion_3.17.1 pillar_1.9.0
[77] stringr_1.5.0 limma_3.56.0
[79] later_1.3.0 dplyr_1.1.2
[81] lattice_0.21-8 rtracklayer_1.60.0
[83] bit_4.0.5 tidyselect_1.2.0
[85] locfit_1.5-9.7 Biostrings_2.68.0
[87] knitr_1.42 gridExtra_2.3
[89] bookdown_0.33 ProtGenerics_1.32.0
[91] edgeR_3.42.0 xfun_0.39
[93] statmod_1.5.0 stringi_1.7.12
[95] lazyeval_0.2.2 yaml_2.3.7
[97] evaluate_0.20 codetools_0.2-19
[99] tibble_3.2.1 BiocManager_1.30.20
[101] graph_1.78.0 cli_3.6.1
[103] xtable_1.8-4 munsell_0.5.0
[105] jquerylib_0.1.4 Rcpp_1.0.10
[107] dir.expiry_1.8.0 png_0.1-8
[109] XML_3.99-0.14 parallel_4.3.0
[111] ellipsis_0.3.2 blob_1.2.4
[113] prettyunits_1.1.1 sparseMatrixStats_1.12.0
[115] bitops_1.0-7 viridisLite_0.4.1
[117] scales_1.2.1 purrr_1.0.1
[119] crayon_1.5.2 rlang_1.1.0
[121] cowplot_1.1.1 KEGGREST_1.40.0 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.