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 4.2.0 RC (2022-04-21 r82226)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 20.04.4 LTS
Matrix products: default
BLAS: /home/biocbuild/bbs-3.16-bioc/R/lib/libRblas.so
LAPACK: /home/biocbuild/bbs-3.16-bioc/R/lib/libRlapack.so
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
attached base packages:
[1] stats4 stats graphics grDevices utils datasets methods
[8] base
other attached packages:
[1] BiocSingular_1.13.0 scran_1.25.0
[3] AnnotationHub_3.5.0 BiocFileCache_2.5.0
[5] dbplyr_2.1.1 scater_1.25.1
[7] ggplot2_3.3.6 scuttle_1.7.2
[9] ensembldb_2.21.1 AnnotationFilter_1.21.0
[11] GenomicFeatures_1.49.4 AnnotationDbi_1.59.1
[13] scRNAseq_2.11.0 SingleCellExperiment_1.19.0
[15] SummarizedExperiment_1.27.1 Biobase_2.57.1
[17] GenomicRanges_1.49.0 GenomeInfoDb_1.33.3
[19] IRanges_2.31.0 S4Vectors_0.35.0
[21] BiocGenerics_0.43.0 MatrixGenerics_1.9.0
[23] matrixStats_0.62.0 BiocStyle_2.25.0
[25] rebook_1.7.0
loaded via a namespace (and not attached):
[1] igraph_1.3.1 lazyeval_0.2.2
[3] BiocParallel_1.31.4 digest_0.6.29
[5] htmltools_0.5.2 viridis_0.6.2
[7] fansi_1.0.3 magrittr_2.0.3
[9] memoise_2.0.1 ScaledMatrix_1.5.0
[11] cluster_2.1.3 limma_3.53.2
[13] Biostrings_2.65.0 prettyunits_1.1.1
[15] colorspace_2.0-3 blob_1.2.3
[17] rappdirs_0.3.3 ggrepel_0.9.1
[19] xfun_0.31 dplyr_1.0.9
[21] crayon_1.5.1 RCurl_1.98-1.6
[23] jsonlite_1.8.0 graph_1.75.0
[25] glue_1.6.2 gtable_0.3.0
[27] zlibbioc_1.43.0 XVector_0.37.0
[29] DelayedArray_0.23.0 scales_1.2.0
[31] edgeR_3.39.1 DBI_1.1.2
[33] Rcpp_1.0.8.3 viridisLite_0.4.0
[35] xtable_1.8-4 progress_1.2.2
[37] dqrng_0.3.0 bit_4.0.4
[39] rsvd_1.0.5 metapod_1.5.0
[41] httr_1.4.3 dir.expiry_1.5.0
[43] ellipsis_0.3.2 farver_2.1.0
[45] pkgconfig_2.0.3 XML_3.99-0.9
[47] CodeDepends_0.6.5 sass_0.4.1
[49] locfit_1.5-9.5 utf8_1.2.2
[51] labeling_0.4.2 tidyselect_1.1.2
[53] rlang_1.0.2 later_1.3.0
[55] munsell_0.5.0 BiocVersion_3.16.0
[57] tools_4.2.0 cachem_1.0.6
[59] cli_3.3.0 generics_0.1.2
[61] RSQLite_2.2.14 ExperimentHub_2.5.0
[63] evaluate_0.15 stringr_1.4.0
[65] fastmap_1.1.0 yaml_2.3.5
[67] knitr_1.39 bit64_4.0.5
[69] purrr_0.3.4 KEGGREST_1.37.0
[71] sparseMatrixStats_1.9.0 mime_0.12
[73] xml2_1.3.3 biomaRt_2.53.2
[75] compiler_4.2.0 beeswarm_0.4.0
[77] filelock_1.0.2 curl_4.3.2
[79] png_0.1-7 interactiveDisplayBase_1.35.0
[81] statmod_1.4.36 tibble_3.1.7
[83] bslib_0.3.1 stringi_1.7.6
[85] highr_0.9 bluster_1.7.0
[87] lattice_0.20-45 ProtGenerics_1.29.0
[89] Matrix_1.4-1 vctrs_0.4.1
[91] pillar_1.7.0 lifecycle_1.0.1
[93] BiocManager_1.30.18 jquerylib_0.1.4
[95] BiocNeighbors_1.15.0 cowplot_1.1.1
[97] bitops_1.0-7 irlba_2.3.5
[99] httpuv_1.6.5 rtracklayer_1.57.0
[101] R6_2.5.1 BiocIO_1.7.1
[103] bookdown_0.26 promises_1.2.0.1
[105] gridExtra_2.3 vipor_0.4.5
[107] codetools_0.2-18 assertthat_0.2.1
[109] rjson_0.2.21 withr_2.5.0
[111] GenomicAlignments_1.33.0 Rsamtools_2.13.2
[113] GenomeInfoDbData_1.2.8 parallel_4.2.0
[115] hms_1.1.1 grid_4.2.0
[117] beachmat_2.13.0 rmarkdown_2.14
[119] DelayedMatrixStats_1.19.0 Rtsne_0.16
[121] shiny_1.7.1 ggbeeswarm_0.6.0
[123] 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.