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
)

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 12.1: 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()

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.

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)

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.

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")

Obligatory $t$-SNE plot of the Bach mammary gland dataset, where each point represents a cell and is colored according to the assigned cluster.

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 Under development (unstable) (2024-10-21 r87258)
Platform: x86_64-pc-linux-gnu
Running under: Ubuntu 24.04.1 LTS

Matrix products: default
BLAS:   /home/biocbuild/bbs-3.21-bioc/R/lib/libRblas.so 
LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.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_1.23.0         scran_1.35.0               
 [3] AnnotationHub_3.15.0        BiocFileCache_2.15.0       
 [5] dbplyr_2.5.0                scater_1.35.0              
 [7] ggplot2_3.5.1               scuttle_1.17.0             
 [9] ensembldb_2.31.0            AnnotationFilter_1.31.0    
[11] GenomicFeatures_1.59.1      AnnotationDbi_1.69.0       
[13] scRNAseq_2.21.0             SingleCellExperiment_1.29.1
[15] SummarizedExperiment_1.37.0 Biobase_2.67.0             
[17] GenomicRanges_1.59.0        GenomeInfoDb_1.43.0        
[19] IRanges_2.41.0              S4Vectors_0.45.1           
[21] BiocGenerics_0.53.1         generics_0.1.3             
[23] MatrixGenerics_1.19.0       matrixStats_1.4.1          
[25] BiocStyle_2.35.0            rebook_1.17.0              

loaded via a namespace (and not attached):
  [1] jsonlite_1.8.9           CodeDepends_0.6.6        magrittr_2.0.3          
  [4] ggbeeswarm_0.7.2         gypsum_1.3.0             farver_2.1.2            
  [7] rmarkdown_2.29           BiocIO_1.17.0            zlibbioc_1.53.0         
 [10] vctrs_0.6.5              memoise_2.0.1            Rsamtools_2.23.0        
 [13] RCurl_1.98-1.16          htmltools_0.5.8.1        S4Arrays_1.7.1          
 [16] curl_6.0.0               BiocNeighbors_2.1.0      Rhdf5lib_1.29.0         
 [19] SparseArray_1.7.1        rhdf5_2.51.0             sass_0.4.9              
 [22] alabaster.base_1.7.1     bslib_0.8.0              alabaster.sce_1.7.0     
 [25] httr2_1.0.6              cachem_1.1.0             GenomicAlignments_1.43.0
 [28] igraph_2.1.1             mime_0.12                lifecycle_1.0.4         
 [31] pkgconfig_2.0.3          rsvd_1.0.5               Matrix_1.7-1            
 [34] R6_2.5.1                 fastmap_1.2.0            GenomeInfoDbData_1.2.13 
 [37] digest_0.6.37            colorspace_2.1-1         dqrng_0.4.1             
 [40] irlba_2.3.5.1            ExperimentHub_2.15.0     RSQLite_2.3.7           
 [43] beachmat_2.23.0          labeling_0.4.3           filelock_1.0.3          
 [46] fansi_1.0.6              httr_1.4.7               abind_1.4-8             
 [49] compiler_4.5.0           bit64_4.5.2              withr_3.0.2             
 [52] BiocParallel_1.41.0      viridis_0.6.5            DBI_1.2.3               
 [55] HDF5Array_1.35.1         alabaster.ranges_1.7.0   alabaster.schemas_1.7.0 
 [58] rappdirs_0.3.3           DelayedArray_0.33.1      bluster_1.17.0          
 [61] rjson_0.2.23             tools_4.5.0              vipor_0.4.7             
 [64] beeswarm_0.4.0           glue_1.8.0               restfulr_0.0.15         
 [67] rhdf5filters_1.19.0      grid_4.5.0               Rtsne_0.17              
 [70] cluster_2.1.6            gtable_0.3.6             metapod_1.15.0          
 [73] ScaledMatrix_1.15.0      utf8_1.2.4               XVector_0.47.0          
 [76] ggrepel_0.9.6            BiocVersion_3.21.1       pillar_1.9.0            
 [79] limma_3.63.2             dplyr_1.1.4              lattice_0.22-6          
 [82] rtracklayer_1.67.0       bit_4.5.0                tidyselect_1.2.1        
 [85] locfit_1.5-9.10          Biostrings_2.75.1        knitr_1.49              
 [88] gridExtra_2.3            bookdown_0.41            ProtGenerics_1.39.0     
 [91] edgeR_4.5.0              xfun_0.49                statmod_1.5.0           
 [94] UCSC.utils_1.3.0         lazyeval_0.2.2           yaml_2.3.10             
 [97] evaluate_1.0.1           codetools_0.2-20         tibble_3.2.1            
[100] alabaster.matrix_1.7.0   BiocManager_1.30.25      graph_1.85.0            
[103] cli_3.6.3                munsell_0.5.1            jquerylib_0.1.4         
[106] Rcpp_1.0.13-1            dir.expiry_1.15.0        png_0.1-8               
[109] XML_3.99-0.17            parallel_4.5.0           blob_1.2.4              
[112] bitops_1.0-9             viridisLite_0.4.2        alabaster.se_1.7.0      
[115] scales_1.3.0             purrr_1.0.2              crayon_1.5.3            
[118] rlang_1.1.4              cowplot_1.1.3            KEGGREST_1.47.0

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.