Chapter 6 Muraro human pancreas (CEL-seq) | Single-Cell Analysis Workflows with Bioconductor

# Chapter 6 Muraro human pancreas (CEL-seq)

## 6.1 Introduction

This performs an analysis of the Muraro et al. (2016) CEL-seq dataset, consisting of human pancreas cells from various donors.

## 6.2 Data loading

    library(scRNAseq)
    sce.muraro <- MuraroPancreasData()

Converting back to Ensembl identifiers.

    library(AnnotationHub)
    edb <- AnnotationHub()[["AH73881"]]
    gene.symb <- sub("__chr.*$", "", rownames(sce.muraro))
    gene.ids <- mapIds(edb, keys=gene.symb,
        keytype="SYMBOL", column="GENEID")

    # Removing duplicated genes or genes without Ensembl IDs.
    keep <- !is.na(gene.ids) & !duplicated(gene.ids)
    sce.muraro <- sce.muraro[keep,]
    rownames(sce.muraro) <- gene.ids[keep]

## 6.3 Quality control

    unfiltered <- sce.muraro

This dataset lacks mitochondrial genes so we will do without. For the one batch that seems to have a high proportion of low-quality cells, we compute an appropriate filter threshold using a shared median and MAD from the other batches (Figure 6.1).

    library(scater)
    stats <- perCellQCMetrics(sce.muraro)
    qc <- quickPerCellQC(stats, percent_subsets="altexps_ERCC_percent",
        batch=sce.muraro$donor, subset=sce.muraro$donor!="D28")
    sce.muraro <- sce.muraro[,!qc$discard]

    colData(unfiltered) <- cbind(colData(unfiltered), stats)
    unfiltered$discard <- qc$discard

    gridExtra::grid.arrange(
        plotColData(unfiltered, x="donor", y="sum", colour_by="discard") +
            scale_y_log10() + ggtitle("Total count"),
        plotColData(unfiltered, x="donor", y="detected", colour_by="discard") +
            scale_y_log10() + ggtitle("Detected features"),
        plotColData(unfiltered, x="donor", y="altexps_ERCC_percent",
            colour_by="discard") + ggtitle("ERCC percent"),
        ncol=2
    )

Figure 6.1: Distribution of each QC metric across cells from each donor in the Muraro pancreas dataset. Each point represents a cell and is colored according to whether that cell was discarded.

We have a look at the causes of removal:

    colSums(as.matrix(qc))

    ##              low_lib_size            low_n_features high_altexps_ERCC_percent
    ##                       663                       700                       738
    ##                   discard
    ##                       773

## 6.4 Normalization

    library(scran)
    set.seed(1000)
    clusters <- quickCluster(sce.muraro)
    sce.muraro <- computeSumFactors(sce.muraro, clusters=clusters)
    sce.muraro <- logNormCounts(sce.muraro)

    summary(sizeFactors(sce.muraro))

    ##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.
    ##  0.0878  0.5411  0.8208  1.0000  1.2108 13.9869

    plot(librarySizeFactors(sce.muraro), sizeFactors(sce.muraro), pch=16,
        xlab="Library size factors", ylab="Deconvolution factors", log="xy")

Figure 6.2: Relationship between the library size factors and the deconvolution size factors in the Muraro pancreas dataset.

## 6.5 Variance modelling

We block on a combined plate and donor factor.

    block <- paste0(sce.muraro$plate, "_", sce.muraro$donor)
    dec.muraro <- modelGeneVarWithSpikes(sce.muraro, "ERCC", block=block)
    top.muraro <- getTopHVGs(dec.muraro, prop=0.1)

    par(mfrow=c(8,4))
    blocked.stats <- dec.muraro$per.block
    for (i in colnames(blocked.stats)) {
        current <- blocked.stats[[i]]
        plot(current$mean, current$total, main=i, pch=16, cex=0.5,
            xlab="Mean of log-expression", ylab="Variance of log-expression")
        curfit <- metadata(current)
        points(curfit$mean, curfit$var, col="red", pch=16)
        curve(curfit$trend(x), col='dodgerblue', add=TRUE, lwd=2)
    }

Figure 6.3: Per-gene variance as a function of the mean for the log-expression values in the Muraro pancreas dataset. Each point represents a gene (black) with the mean-variance trend (blue) fitted to the spike-in transcripts (red) separately for each donor.

## 6.6 Data integration

    library(batchelor)
    set.seed(1001010)
    merged.muraro <- fastMNN(sce.muraro, subset.row=top.muraro,
        batch=sce.muraro$donor)

We use the proportion of variance lost as a diagnostic measure:

    metadata(merged.muraro)$merge.info$lost.var

    ##           D28      D29      D30     D31
    ## [1,] 0.060847 0.024121 0.000000 0.00000
    ## [2,] 0.002646 0.003018 0.062421 0.00000
    ## [3,] 0.003449 0.002641 0.002598 0.08162

## 6.7 Dimensionality reduction

    set.seed(100111)
    merged.muraro <- runTSNE(merged.muraro, dimred="corrected")

## 6.8 Clustering

    snn.gr <- buildSNNGraph(merged.muraro, use.dimred="corrected")
    colLabels(merged.muraro) <- factor(igraph::cluster_walktrap(snn.gr)$membership)

    tab <- table(Cluster=colLabels(merged.muraro), CellType=sce.muraro$label)
    library(pheatmap)
    pheatmap(log10(tab+10), color=viridis::viridis(100))

Figure 6.4: Heatmap of the frequency of cells from each cell type label in each cluster.

    table(Cluster=colLabels(merged.muraro), Donor=merged.muraro$batch)

    ##        Donor
    ## Cluster D28 D29 D30 D31
    ##      1  104   6  57 112
    ##      2   59  21  77  97
    ##      3   12  75  64  43
    ##      4   28 149 126 120
    ##      5   87 261 277 214
    ##      6   21   7  54  26
    ##      7    1   6   6  37
    ##      8    6   6   5   2
    ##      9   11  68   5  30
    ##      10   4   2   5   8

    gridExtra::grid.arrange(
        plotTSNE(merged.muraro, colour_by="label"),
        plotTSNE(merged.muraro, colour_by="batch"),
        ncol=2
    )

Figure 6.5: Obligatory \(t\)-SNE plots of the Muraro pancreas dataset. Each point represents a cell that is colored by cluster (left) or batch (right).

## 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] pheatmap_1.0.13             batchelor_1.26.0
     [3] scran_1.38.0                scater_1.38.0
     [5] ggplot2_4.0.0               scuttle_1.20.0
     [7] ensembldb_2.34.0            AnnotationFilter_1.34.0
     [9] GenomicFeatures_1.62.0      AnnotationDbi_1.72.0
    [11] AnnotationHub_4.0.0         BiocFileCache_3.0.0
    [13] dbplyr_2.5.1                scRNAseq_2.24.0
    [15] SingleCellExperiment_1.32.0 SummarizedExperiment_1.40.0
    [17] Biobase_2.70.0              GenomicRanges_1.62.0
    [19] Seqinfo_1.0.0               IRanges_2.44.0
    [21] S4Vectors_0.48.0            BiocGenerics_0.56.0
    [23] generics_0.1.4              MatrixGenerics_1.22.0
    [25] matrixStats_1.5.0           BiocStyle_2.38.0
    [27] rebook_1.20.0

    loaded via a namespace (and not attached):
      [1] RColorBrewer_1.1-3        jsonlite_2.0.0
      [3] CodeDepends_0.6.6         magrittr_2.0.4
      [5] ggbeeswarm_0.7.2          gypsum_1.6.0
      [7] farver_2.1.2              rmarkdown_2.30
      [9] BiocIO_1.20.0             vctrs_0.6.5
     [11] DelayedMatrixStats_1.32.0 memoise_2.0.1
     [13] Rsamtools_2.26.0          RCurl_1.98-1.17
     [15] htmltools_0.5.8.1         S4Arrays_1.10.0
     [17] curl_7.0.0                BiocNeighbors_2.4.0
     [19] Rhdf5lib_1.32.0           SparseArray_1.10.1
     [21] rhdf5_2.54.0              sass_0.4.10
     [23] alabaster.base_1.10.0     bslib_0.9.0
     [25] alabaster.sce_1.10.0      httr2_1.2.1
     [27] cachem_1.1.0              ResidualMatrix_1.20.0
     [29] GenomicAlignments_1.46.0  igraph_2.2.1
     [31] lifecycle_1.0.4           pkgconfig_2.0.3
     [33] rsvd_1.0.5                Matrix_1.7-4
     [35] R6_2.6.1                  fastmap_1.2.0
     [37] digest_0.6.37             dqrng_0.4.1
     [39] irlba_2.3.5.1             ExperimentHub_3.0.0
     [41] RSQLite_2.4.3             beachmat_2.26.0
     [43] labeling_0.4.3            filelock_1.0.3
     [45] httr_1.4.7                abind_1.4-8
     [47] compiler_4.5.1            bit64_4.6.0-1
     [49] withr_3.0.2               S7_0.2.0
     [51] BiocParallel_1.44.0       viridis_0.6.5
     [53] DBI_1.2.3                 HDF5Array_1.38.0
     [55] alabaster.ranges_1.10.0   alabaster.schemas_1.10.0
     [57] rappdirs_0.3.3            DelayedArray_0.36.0
     [59] bluster_1.20.0            rjson_0.2.23
     [61] tools_4.5.1               vipor_0.4.7
     [63] beeswarm_0.4.0            glue_1.8.0
     [65] h5mread_1.2.0             restfulr_0.0.16
     [67] rhdf5filters_1.22.0       grid_4.5.1
     [69] Rtsne_0.17                cluster_2.1.8.1
     [71] gtable_0.3.6              metapod_1.18.0
     [73] BiocSingular_1.26.0       ScaledMatrix_1.18.0
     [75] XVector_0.50.0            ggrepel_0.9.6
     [77] BiocVersion_3.22.0        pillar_1.11.1
     [79] limma_3.66.0              dplyr_1.1.4
     [81] lattice_0.22-7            rtracklayer_1.70.0
     [83] bit_4.6.0                 tidyselect_1.2.1
     [85] locfit_1.5-9.12           Biostrings_2.78.0
     [87] knitr_1.50                gridExtra_2.3
     [89] bookdown_0.45             ProtGenerics_1.42.0
     [91] edgeR_4.8.0               xfun_0.54
     [93] statmod_1.5.1             UCSC.utils_1.6.0
     [95] lazyeval_0.2.2            yaml_2.3.10
     [97] evaluate_1.0.5            codetools_0.2-20
     [99] cigarillo_1.0.0           tibble_3.3.0
    [101] alabaster.matrix_1.10.0   BiocManager_1.30.26
    [103] graph_1.88.0              cli_3.6.5
    [105] jquerylib_0.1.4           dichromat_2.0-0.1
    [107] Rcpp_1.1.0                GenomeInfoDb_1.46.0
    [109] dir.expiry_1.18.0         png_0.1-8
    [111] XML_3.99-0.19             parallel_4.5.1
    [113] blob_1.2.4                sparseMatrixStats_1.22.0
    [115] bitops_1.0-9              viridisLite_0.4.2
    [117] alabaster.se_1.10.0       scales_1.4.0
    [119] purrr_1.1.0               crayon_1.5.3
    [121] rlang_1.1.6               cowplot_1.2.0
    [123] KEGGREST_1.50.0

### References

Muraro, M. J., G. Dharmadhikari, D. Grun, N. Groen, T. Dielen, E. Jansen, L. van Gurp, et al. 2016. “A Single-Cell Transcriptome Atlas of the Human Pancreas.” Cell Syst 3 (4): 385–94.