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
Converting back to Ensembl identifiers.
library(AnnotationHub)
edb <- AnnotationHub()[["AH73881"]]
gene.symb <- sub("__chr.*$", "", rownames(sce.muraro))
gene.ids <- mapIds(edb, keys=gene.symb,
keytype="SYMBOL", column="GENEID")
# Removing duplicated genes or genes without Ensembl IDs.
keep <- !is.na(gene.ids) & !duplicated(gene.ids)
sce.muraro <- sce.muraro[keep,]
rownames(sce.muraro) <- gene.ids[keep]6.3 Quality control
This dataset lacks mitochondrial genes so we will do without. For the one batch that seems to have a high proportion of low-quality cells, we compute an appropriate filter threshold using a shared median and MAD from the other batches (Figure 6.1).
library(scater)
stats <- perCellQCMetrics(sce.muraro)
qc <- quickPerCellQC(stats, percent_subsets="altexps_ERCC_percent",
batch=sce.muraro$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:
## low_lib_size low_n_features high_altexps_ERCC_percent
## 663 700 738
## discard
## 7736.4 Normalization
library(scran)
set.seed(1000)
clusters <- quickCluster(sce.muraro)
sce.muraro <- computeSumFactors(sce.muraro, clusters=clusters)
sce.muraro <- logNormCounts(sce.muraro)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.088 0.541 0.821 1.000 1.211 13.987plot(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$donor)We use the proportion of variance lost as a diagnostic measure:
## D28 D29 D30 D31
## [1,] 0.060847 0.024121 0.000000 0.00000
## [2,] 0.002646 0.003018 0.062421 0.00000
## [3,] 0.003449 0.002641 0.002598 0.081626.8 Clustering
snn.gr <- buildSNNGraph(merged.muraro, use.dimred="corrected")
colLabels(merged.muraro) <- factor(igraph::cluster_walktrap(snn.gr)$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.
## 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 8gridExtra::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 4.2.1 (2022-06-23)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 20.04.5 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] pheatmap_1.0.12 batchelor_1.14.0
[3] scran_1.26.0 scater_1.26.0
[5] ggplot2_3.3.6 scuttle_1.8.0
[7] ensembldb_2.22.0 AnnotationFilter_1.22.0
[9] GenomicFeatures_1.50.0 AnnotationDbi_1.60.0
[11] AnnotationHub_3.6.0 BiocFileCache_2.6.0
[13] dbplyr_2.2.1 scRNAseq_2.11.0
[15] SingleCellExperiment_1.20.0 SummarizedExperiment_1.28.0
[17] Biobase_2.58.0 GenomicRanges_1.50.0
[19] GenomeInfoDb_1.34.0 IRanges_2.32.0
[21] S4Vectors_0.36.0 BiocGenerics_0.44.0
[23] MatrixGenerics_1.10.0 matrixStats_0.62.0
[25] BiocStyle_2.26.0 rebook_1.8.0
loaded via a namespace (and not attached):
[1] igraph_1.3.5 lazyeval_0.2.2
[3] BiocParallel_1.32.0 digest_0.6.30
[5] htmltools_0.5.3 viridis_0.6.2
[7] fansi_1.0.3 magrittr_2.0.3
[9] memoise_2.0.1 ScaledMatrix_1.6.0
[11] cluster_2.1.4 limma_3.54.0
[13] Biostrings_2.66.0 prettyunits_1.1.1
[15] colorspace_2.0-3 blob_1.2.3
[17] rappdirs_0.3.3 ggrepel_0.9.1
[19] xfun_0.34 dplyr_1.0.10
[21] crayon_1.5.2 RCurl_1.98-1.9
[23] jsonlite_1.8.3 graph_1.76.0
[25] glue_1.6.2 gtable_0.3.1
[27] zlibbioc_1.44.0 XVector_0.38.0
[29] DelayedArray_0.24.0 BiocSingular_1.14.0
[31] scales_1.2.1 edgeR_3.40.0
[33] DBI_1.1.3 Rcpp_1.0.9
[35] viridisLite_0.4.1 xtable_1.8-4
[37] progress_1.2.2 dqrng_0.3.0
[39] bit_4.0.4 rsvd_1.0.5
[41] ResidualMatrix_1.8.0 metapod_1.6.0
[43] httr_1.4.4 RColorBrewer_1.1-3
[45] dir.expiry_1.6.0 ellipsis_0.3.2
[47] pkgconfig_2.0.3 XML_3.99-0.12
[49] farver_2.1.1 CodeDepends_0.6.5
[51] sass_0.4.2 locfit_1.5-9.6
[53] utf8_1.2.2 labeling_0.4.2
[55] tidyselect_1.2.0 rlang_1.0.6
[57] later_1.3.0 munsell_0.5.0
[59] BiocVersion_3.16.0 tools_4.2.1
[61] cachem_1.0.6 cli_3.4.1
[63] generics_0.1.3 RSQLite_2.2.18
[65] ExperimentHub_2.6.0 evaluate_0.17
[67] stringr_1.4.1 fastmap_1.1.0
[69] yaml_2.3.6 knitr_1.40
[71] bit64_4.0.5 purrr_0.3.5
[73] KEGGREST_1.38.0 sparseMatrixStats_1.10.0
[75] mime_0.12 xml2_1.3.3
[77] biomaRt_2.54.0 compiler_4.2.1
[79] beeswarm_0.4.0 filelock_1.0.2
[81] curl_4.3.3 png_0.1-7
[83] interactiveDisplayBase_1.36.0 statmod_1.4.37
[85] tibble_3.1.8 bslib_0.4.0
[87] stringi_1.7.8 highr_0.9
[89] bluster_1.8.0 lattice_0.20-45
[91] ProtGenerics_1.30.0 Matrix_1.5-1
[93] vctrs_0.5.0 pillar_1.8.1
[95] lifecycle_1.0.3 BiocManager_1.30.19
[97] jquerylib_0.1.4 BiocNeighbors_1.16.0
[99] cowplot_1.1.1 bitops_1.0-7
[101] irlba_2.3.5.1 httpuv_1.6.6
[103] rtracklayer_1.58.0 R6_2.5.1
[105] BiocIO_1.8.0 bookdown_0.29
[107] promises_1.2.0.1 gridExtra_2.3
[109] vipor_0.4.5 codetools_0.2-18
[111] assertthat_0.2.1 rjson_0.2.21
[113] withr_2.5.0 GenomicAlignments_1.34.0
[115] Rsamtools_2.14.0 GenomeInfoDbData_1.2.9
[117] parallel_4.2.1 hms_1.1.2
[119] grid_4.2.1 beachmat_2.14.0
[121] rmarkdown_2.17 DelayedMatrixStats_1.20.0
[123] Rtsne_0.16 shiny_1.7.3
[125] ggbeeswarm_0.6.0 restfulr_0.0.15 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.