Chapter 12 Bach mouse mammary gland (10X Genomics) | Single-Cell Analysis Workflows with Bioconductor

# 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

    library(scRNAseq)
    sce.mam <- BachMammaryData(samples="G_1")

    library(scater)
    rownames(sce.mam) <- uniquifyFeatureNames(
        rowData(sce.mam)$Ensembl, rowData(sce.mam)$Symbol)

    library(AnnotationHub)
    ens.mm.v97 <- AnnotationHub()[["AH73905"]]
    rowData(sce.mam)$SEQNAME <- mapIds(ens.mm.v97, keys=rowData(sce.mam)$Ensembl,
        keytype="GENEID", column="SEQNAME")

## 12.3 Quality control

    unfiltered <- sce.mam

    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.

    plotColData(unfiltered, x="sum", y="subsets_Mito_percent",
        colour_by="discard") + scale_x_log10()

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.

    colSums(as.matrix(qc))

    ##              low_lib_size            low_n_features high_subsets_Mito_percent
    ##                         0                         0                       143
    ##                   discard
    ##                       143

## 12.4 Normalization

    library(scran)
    set.seed(101000110)
    clusters <- quickCluster(sce.mam)
    sce.mam <- computeSumFactors(sce.mam, clusters=clusters)
    sce.mam <- logNormCounts(sce.mam)

    summary(sizeFactors(sce.mam))

    ##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.
    ##   0.264   0.520   0.752   1.000   1.207  10.790

    plot(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")

    ncol(reducedDim(sce.mam, "PCA"))

    ## [1] 15

## 12.7 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)

    table(colLabels(sce.mam))

    ##
    ##   1   2   3   4   5   6   7   8   9  10
    ## 550 847 639 477  54  88  39  22  32  24

    plotTSNE(sce.mam, colour_by="label")

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 4.5.1 Patched (2025-08-23 r88802)
    Platform: x86_64-pc-linux-gnu
    Running under: Ubuntu 24.04.3 LTS

    Matrix products: default
    BLAS:   /home/biocbuild/bbs-3.22-bioc/R/lib/libRblas.so
    LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.12.0  LAPACK version 3.12.0

    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_1.26.0         scran_1.38.0
     [3] AnnotationHub_4.0.0         BiocFileCache_3.0.0
     [5] dbplyr_2.5.1                scater_1.38.0
     [7] ggplot2_4.0.0               scuttle_1.20.0
     [9] ensembldb_2.34.0            AnnotationFilter_1.34.0
    [11] GenomicFeatures_1.62.0      AnnotationDbi_1.72.0
    [13] scRNAseq_2.24.0             SingleCellExperiment_1.32.0
    [15] SummarizedExperiment_1.40.0 Biobase_2.70.0
    [17] GenomicRanges_1.62.0        Seqinfo_1.0.0
    [19] IRanges_2.44.0              S4Vectors_0.48.0
    [21] BiocGenerics_0.56.0         generics_0.1.4
    [23] MatrixGenerics_1.22.0       matrixStats_1.5.0
    [25] BiocStyle_2.38.0            rebook_1.20.0

    loaded via a namespace (and not attached):
      [1] RColorBrewer_1.1-3       jsonlite_2.0.0           CodeDepends_0.6.6
      [4] magrittr_2.0.4           ggbeeswarm_0.7.2         gypsum_1.6.0
      [7] farver_2.1.2             rmarkdown_2.30           BiocIO_1.20.0
     [10] vctrs_0.6.5              memoise_2.0.1            Rsamtools_2.26.0
     [13] RCurl_1.98-1.17          htmltools_0.5.8.1        S4Arrays_1.10.0
     [16] curl_7.0.0               BiocNeighbors_2.4.0      Rhdf5lib_1.32.0
     [19] SparseArray_1.10.1       rhdf5_2.54.0             sass_0.4.10
     [22] alabaster.base_1.10.0    bslib_0.9.0              alabaster.sce_1.10.0
     [25] httr2_1.2.1              cachem_1.1.0             GenomicAlignments_1.46.0
     [28] igraph_2.2.1             lifecycle_1.0.4          pkgconfig_2.0.3
     [31] rsvd_1.0.5               Matrix_1.7-4             R6_2.6.1
     [34] fastmap_1.2.0            digest_0.6.37            dqrng_0.4.1
     [37] irlba_2.3.5.1            ExperimentHub_3.0.0      RSQLite_2.4.3
     [40] beachmat_2.26.0          labeling_0.4.3           filelock_1.0.3
     [43] httr_1.4.7               abind_1.4-8              compiler_4.5.1
     [46] bit64_4.6.0-1            withr_3.0.2              S7_0.2.0
     [49] BiocParallel_1.44.0      viridis_0.6.5            DBI_1.2.3
     [52] HDF5Array_1.38.0         alabaster.ranges_1.10.0  alabaster.schemas_1.10.0
     [55] rappdirs_0.3.3           DelayedArray_0.36.0      bluster_1.20.0
     [58] rjson_0.2.23             tools_4.5.1              vipor_0.4.7
     [61] beeswarm_0.4.0           glue_1.8.0               h5mread_1.2.0
     [64] restfulr_0.0.16          rhdf5filters_1.22.0      grid_4.5.1
     [67] Rtsne_0.17               cluster_2.1.8.1          gtable_0.3.6
     [70] metapod_1.18.0           ScaledMatrix_1.18.0      XVector_0.50.0
     [73] ggrepel_0.9.6            BiocVersion_3.22.0       pillar_1.11.1
     [76] limma_3.66.0             dplyr_1.1.4              lattice_0.22-7
     [79] rtracklayer_1.70.0       bit_4.6.0                tidyselect_1.2.1
     [82] locfit_1.5-9.12          Biostrings_2.78.0        knitr_1.50
     [85] gridExtra_2.3            bookdown_0.45            ProtGenerics_1.42.0
     [88] edgeR_4.8.0              xfun_0.54                statmod_1.5.1
     [91] UCSC.utils_1.6.0         lazyeval_0.2.2           yaml_2.3.10
     [94] evaluate_1.0.5           codetools_0.2-20         cigarillo_1.0.0
     [97] tibble_3.3.0             alabaster.matrix_1.10.0  BiocManager_1.30.26
    [100] graph_1.88.0             cli_3.6.5                jquerylib_0.1.4
    [103] dichromat_2.0-0.1        Rcpp_1.1.0               GenomeInfoDb_1.46.0
    [106] dir.expiry_1.18.0        png_0.1-8                XML_3.99-0.19
    [109] parallel_4.5.1           blob_1.2.4               bitops_1.0-9
    [112] viridisLite_0.4.2        alabaster.se_1.10.0      scales_1.4.0
    [115] purrr_1.1.0              crayon_1.5.3             rlang_1.1.6
    [118] cowplot_1.2.0            KEGGREST_1.50.0

### 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.