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 4.3.0 RC (2023-04-13 r84269)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 22.04.2 LTS
Matrix products: default
BLAS: /home/biocbuild/bbs-3.17-bioc/R/lib/libRblas.so
LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.10.0
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_1.0.12 batchelor_1.16.0
[3] scran_1.28.0 scater_1.28.0
[5] ggplot2_3.4.2 scuttle_1.10.0
[7] ensembldb_2.24.0 AnnotationFilter_1.24.0
[9] GenomicFeatures_1.52.0 AnnotationDbi_1.62.0
[11] AnnotationHub_3.8.0 BiocFileCache_2.8.0
[13] dbplyr_2.3.2 scRNAseq_2.13.0
[15] SingleCellExperiment_1.22.0 SummarizedExperiment_1.30.0
[17] Biobase_2.60.0 GenomicRanges_1.52.0
[19] GenomeInfoDb_1.36.0 IRanges_2.34.0
[21] S4Vectors_0.38.0 BiocGenerics_0.46.0
[23] MatrixGenerics_1.12.0 matrixStats_0.63.0
[25] BiocStyle_2.28.0 rebook_1.10.0
loaded via a namespace (and not attached):
[1] RColorBrewer_1.1-3 jsonlite_1.8.4
[3] CodeDepends_0.6.5 magrittr_2.0.3
[5] ggbeeswarm_0.7.1 farver_2.1.1
[7] rmarkdown_2.21 BiocIO_1.10.0
[9] zlibbioc_1.46.0 vctrs_0.6.2
[11] memoise_2.0.1 Rsamtools_2.16.0
[13] DelayedMatrixStats_1.22.0 RCurl_1.98-1.12
[15] htmltools_0.5.5 progress_1.2.2
[17] curl_5.0.0 BiocNeighbors_1.18.0
[19] sass_0.4.5 bslib_0.4.2
[21] cachem_1.0.7 ResidualMatrix_1.10.0
[23] GenomicAlignments_1.36.0 igraph_1.4.2
[25] mime_0.12 lifecycle_1.0.3
[27] pkgconfig_2.0.3 rsvd_1.0.5
[29] Matrix_1.5-4 R6_2.5.1
[31] fastmap_1.1.1 GenomeInfoDbData_1.2.10
[33] shiny_1.7.4 digest_0.6.31
[35] colorspace_2.1-0 dqrng_0.3.0
[37] irlba_2.3.5.1 ExperimentHub_2.8.0
[39] RSQLite_2.3.1 beachmat_2.16.0
[41] labeling_0.4.2 filelock_1.0.2
[43] fansi_1.0.4 httr_1.4.5
[45] compiler_4.3.0 bit64_4.0.5
[47] withr_2.5.0 BiocParallel_1.34.0
[49] viridis_0.6.2 DBI_1.1.3
[51] highr_0.10 biomaRt_2.56.0
[53] rappdirs_0.3.3 DelayedArray_0.26.0
[55] bluster_1.10.0 rjson_0.2.21
[57] tools_4.3.0 vipor_0.4.5
[59] beeswarm_0.4.0 interactiveDisplayBase_1.38.0
[61] httpuv_1.6.9 glue_1.6.2
[63] restfulr_0.0.15 promises_1.2.0.1
[65] grid_4.3.0 Rtsne_0.16
[67] cluster_2.1.4 generics_0.1.3
[69] gtable_0.3.3 hms_1.1.3
[71] metapod_1.8.0 BiocSingular_1.16.0
[73] ScaledMatrix_1.8.0 xml2_1.3.3
[75] utf8_1.2.3 XVector_0.40.0
[77] ggrepel_0.9.3 BiocVersion_3.17.1
[79] pillar_1.9.0 stringr_1.5.0
[81] limma_3.56.0 later_1.3.0
[83] dplyr_1.1.2 lattice_0.21-8
[85] rtracklayer_1.60.0 bit_4.0.5
[87] tidyselect_1.2.0 locfit_1.5-9.7
[89] Biostrings_2.68.0 knitr_1.42
[91] gridExtra_2.3 bookdown_0.33
[93] ProtGenerics_1.32.0 edgeR_3.42.0
[95] xfun_0.39 statmod_1.5.0
[97] stringi_1.7.12 lazyeval_0.2.2
[99] yaml_2.3.7 evaluate_0.20
[101] codetools_0.2-19 tibble_3.2.1
[103] BiocManager_1.30.20 graph_1.78.0
[105] cli_3.6.1 xtable_1.8-4
[107] munsell_0.5.0 jquerylib_0.1.4
[109] Rcpp_1.0.10 dir.expiry_1.8.0
[111] png_0.1-8 XML_3.99-0.14
[113] parallel_4.3.0 ellipsis_0.3.2
[115] blob_1.2.4 prettyunits_1.1.1
[117] sparseMatrixStats_1.12.0 bitops_1.0-7
[119] viridisLite_0.4.1 scales_1.2.1
[121] purrr_1.0.1 crayon_1.5.2
[123] rlang_1.1.0 cowplot_1.1.1
[125] KEGGREST_1.40.0 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.