Chapter 31 Muraro human pancreas (CEL-seq)
31.1 Introduction
This performs an analysis of the Muraro et al. (2016) CEL-seq dataset, consisting of human pancreas cells from various donors.
31.2 Data loading
Converting back to Ensembl identifiers.
library(AnnotationHub)
edb <- AnnotationHub()[["AH73881"]]
gene.symb <- sub("__chr.*$", "", rownames(sce.muraro))
gene.ids <- mapIds(edb, keys=gene.symb,
keytype="SYMBOL", column="GENEID")
# Removing duplicated genes or genes without Ensembl IDs.
keep <- !is.na(gene.ids) & !duplicated(gene.ids)
sce.muraro <- sce.muraro[keep,]
rownames(sce.muraro) <- gene.ids[keep]31.3 Quality control
This dataset lacks mitochondrial genes so we will do without. For the one batch that seems to have a high proportion of low-quality cells, we compute an appropriate filter threshold using a shared median and MAD from the other batches (Figure 31.1).
library(scater)
stats <- perCellQCMetrics(sce.muraro)
qc <- quickPerCellQC(stats, percent_subsets="altexps_ERCC_percent",
batch=sce.muraro$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 31.1: 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
## 77331.4 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 31.2: Relationship between the library size factors and the deconvolution size factors in the Muraro pancreas dataset.
31.5 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 31.3: 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.
31.6 Data integration
library(batchelor)
set.seed(1001010)
merged.muraro <- fastMNN(sce.muraro, subset.row=top.muraro,
batch=sce.muraro$donor)We use the proportion of variance lost as a diagnostic measure:
## D28 D29 D30 D31
## [1,] 0.060847 0.024121 0.000000 0.00000
## [2,] 0.002646 0.003018 0.062421 0.00000
## [3,] 0.003449 0.002641 0.002598 0.0816231.7 Dimensionality reduction
31.8 Clustering
snn.gr <- buildSNNGraph(merged.muraro, use.dimred="corrected")
colLabels(merged.muraro) <- factor(igraph::cluster_walktrap(snn.gr)$membership)tab <- table(Cluster=colLabels(merged.muraro), CellType=sce.muraro$label)
library(pheatmap)
pheatmap(log10(tab+10), color=viridis::viridis(100))
Figure 31.4: 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 31.5: 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 4.0.3 (2020-10-10)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 20.04.1 LTS
Matrix products: default
BLAS: /home/biocbuild/bbs-3.12-bioc/R/lib/libRblas.so
LAPACK: /home/biocbuild/bbs-3.12-bioc/R/lib/libRlapack.so
locale:
[1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
[3] LC_TIME=en_US.UTF-8 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] parallel stats4 stats graphics grDevices utils datasets
[8] methods base
other attached packages:
[1] pheatmap_1.0.12 batchelor_1.6.0
[3] scran_1.18.0 scater_1.18.0
[5] ggplot2_3.3.2 ensembldb_2.14.0
[7] AnnotationFilter_1.14.0 GenomicFeatures_1.42.0
[9] AnnotationDbi_1.52.0 AnnotationHub_2.22.0
[11] BiocFileCache_1.14.0 dbplyr_1.4.4
[13] scRNAseq_2.4.0 SingleCellExperiment_1.12.0
[15] SummarizedExperiment_1.20.0 Biobase_2.50.0
[17] GenomicRanges_1.42.0 GenomeInfoDb_1.26.0
[19] IRanges_2.24.0 S4Vectors_0.28.0
[21] BiocGenerics_0.36.0 MatrixGenerics_1.2.0
[23] matrixStats_0.57.0 BiocStyle_2.18.0
[25] rebook_1.0.0
loaded via a namespace (and not attached):
[1] igraph_1.2.6 lazyeval_0.2.2
[3] BiocParallel_1.24.0 digest_0.6.27
[5] htmltools_0.5.0 viridis_0.5.1
[7] magrittr_1.5 memoise_1.1.0
[9] limma_3.46.0 Biostrings_2.58.0
[11] askpass_1.1 prettyunits_1.1.1
[13] colorspace_1.4-1 blob_1.2.1
[15] rappdirs_0.3.1 xfun_0.19
[17] dplyr_1.0.2 callr_3.5.1
[19] crayon_1.3.4 RCurl_1.98-1.2
[21] graph_1.68.0 glue_1.4.2
[23] gtable_0.3.0 zlibbioc_1.36.0
[25] XVector_0.30.0 DelayedArray_0.16.0
[27] BiocSingular_1.6.0 scales_1.1.1
[29] DBI_1.1.0 edgeR_3.32.0
[31] Rcpp_1.0.5 viridisLite_0.3.0
[33] xtable_1.8-4 progress_1.2.2
[35] dqrng_0.2.1 bit_4.0.4
[37] rsvd_1.0.3 ResidualMatrix_1.0.0
[39] httr_1.4.2 RColorBrewer_1.1-2
[41] ellipsis_0.3.1 pkgconfig_2.0.3
[43] XML_3.99-0.5 farver_2.0.3
[45] scuttle_1.0.0 CodeDepends_0.6.5
[47] locfit_1.5-9.4 tidyselect_1.1.0
[49] labeling_0.4.2 rlang_0.4.8
[51] later_1.1.0.1 munsell_0.5.0
[53] BiocVersion_3.12.0 tools_4.0.3
[55] generics_0.1.0 RSQLite_2.2.1
[57] ExperimentHub_1.16.0 evaluate_0.14
[59] stringr_1.4.0 fastmap_1.0.1
[61] yaml_2.2.1 processx_3.4.4
[63] knitr_1.30 bit64_4.0.5
[65] purrr_0.3.4 sparseMatrixStats_1.2.0
[67] mime_0.9 xml2_1.3.2
[69] biomaRt_2.46.0 compiler_4.0.3
[71] beeswarm_0.2.3 curl_4.3
[73] interactiveDisplayBase_1.28.0 tibble_3.0.4
[75] statmod_1.4.35 stringi_1.5.3
[77] highr_0.8 ps_1.4.0
[79] lattice_0.20-41 bluster_1.0.0
[81] ProtGenerics_1.22.0 Matrix_1.2-18
[83] vctrs_0.3.4 pillar_1.4.6
[85] lifecycle_0.2.0 BiocManager_1.30.10
[87] BiocNeighbors_1.8.0 cowplot_1.1.0
[89] bitops_1.0-6 irlba_2.3.3
[91] httpuv_1.5.4 rtracklayer_1.50.0
[93] R6_2.5.0 bookdown_0.21
[95] promises_1.1.1 gridExtra_2.3
[97] vipor_0.4.5 codetools_0.2-16
[99] assertthat_0.2.1 openssl_1.4.3
[101] withr_2.3.0 GenomicAlignments_1.26.0
[103] Rsamtools_2.6.0 GenomeInfoDbData_1.2.4
[105] hms_0.5.3 grid_4.0.3
[107] beachmat_2.6.0 rmarkdown_2.5
[109] DelayedMatrixStats_1.12.0 Rtsne_0.15
[111] shiny_1.5.0 ggbeeswarm_0.6.0 Bibliography
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.