Chapter 28 Human pancreas dataset (Muraro)
28.1 Introduction
This performs an analysis of the Muraro et al. (2016) CEL-seq dataset, consisting of human pancreas cells from various donors.
28.2 Analysis code
28.2.1 Data loading
28.2.2 Gene annotation
Converting back to Ensembl identifiers.
library(AnnotationHub)
edb <- AnnotationHub()[["AH73881"]]
gene.symb <- sub("__chr.*$", "", rownames(sce.muraro))
gene.ids <- mapIds(edb, keys=gene.symb,
keytype="SYMBOL", column="GENEID")
# Removing duplicated genes or genes without Ensembl IDs.
keep <- !is.na(gene.ids) & !duplicated(gene.ids)
sce.muraro <- sce.muraro[keep,]
rownames(sce.muraro) <- gene.ids[keep]28.2.3 Quality control
This dataset lacks mitochondrial genes so we will do without.
28.2.4 Normalization
28.2.5 Variance modelling
We block on a combined plate and donor factor.
28.2.6 Data integration
28.2.7 Dimensionality reduction
28.3 Results
28.3.1 Quality control statistics
colData(unfiltered) <- cbind(colData(unfiltered), stats)
unfiltered$discard <- qc$discard
gridExtra::grid.arrange(
plotColData(unfiltered, x="donor", y="sum", colour_by="discard") +
scale_y_log10() + ggtitle("Total count"),
plotColData(unfiltered, x="donor", y="detected", colour_by="discard") +
scale_y_log10() + ggtitle("Detected features"),
plotColData(unfiltered, x="donor", y="altexps_ERCC_percent",
colour_by="discard") + ggtitle("ERCC percent"),
ncol=2
)
## low_lib_size low_n_features high_altexps_ERCC_percent
## 611 669 696
## discard
## 72628.3.2 Normalization
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.080 0.538 0.822 1.000 1.217 14.315plot(librarySizeFactors(sce.muraro), sizeFactors(sce.muraro), pch=16,
xlab="Library size factors", ylab="Deconvolution factors", log="xy")
28.3.3 Variance modelling
par(mfrow=c(8,4))
blocked.stats <- dec.muraro$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)
}
28.3.4 Data integration
## D28 D29 D30 D31
## [1,] 0.061236 0.024182 0.000000 0.00000
## [2,] 0.002639 0.003242 0.062255 0.00000
## [3,] 0.003409 0.002588 0.002575 0.0806328.3.5 Clustering
## CellType
## Cluster acinar alpha beta delta duct endothelial epsilon mesenchymal pp unclear
## 1 217 1 3 0 8 0 0 0 2 0
## 2 0 5 5 0 212 0 0 1 0 4
## 3 0 0 0 186 0 0 0 0 0 0
## 4 0 17 385 3 0 0 0 0 0 0
## 5 0 778 2 1 0 0 3 0 0 0
## 6 1 0 2 0 6 1 0 79 0 0
## 7 0 0 1 0 13 0 0 0 0 0
## 8 0 1 42 1 3 0 0 0 0 0
## 9 0 1 6 0 0 0 0 0 97 0
## 10 0 0 0 0 0 19 0 0 0 0## Donor
## Cluster D28 D29 D30 D31
## 1 108 6 57 113
## 2 57 21 79 97
## 3 13 75 65 43
## 4 28 148 129 121
## 5 88 263 281 222
## 6 22 7 54 26
## 7 7 6 6 6
## 8 1 6 7 38
## 9 11 70 6 38
## 10 5 2 5 9
Session Info
R version 3.6.1 (2019-07-05)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 14.04.5 LTS
Matrix products: default
BLAS: /home/ramezqui/Rbuild/danbuild/R-3.6.1/lib/libRblas.so
LAPACK: /home/ramezqui/Rbuild/danbuild/R-3.6.1/lib/libRlapack.so
locale:
[1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C LC_TIME=en_US.UTF-8
[4] LC_COLLATE=C LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
[7] LC_PAPER=en_US.UTF-8 LC_NAME=C LC_ADDRESS=C
[10] LC_TELEPHONE=C LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
attached base packages:
[1] parallel stats4 stats graphics grDevices utils datasets methods base
other attached packages:
[1] batchelor_1.2.2 scran_1.14.3 scater_1.14.3
[4] ggplot2_3.2.1 ensembldb_2.10.0 AnnotationFilter_1.10.0
[7] GenomicFeatures_1.38.0 AnnotationDbi_1.48.0 AnnotationHub_2.18.0
[10] BiocFileCache_1.10.2 dbplyr_1.4.2 scRNAseq_2.0.2
[13] SingleCellExperiment_1.8.0 SummarizedExperiment_1.16.0 DelayedArray_0.12.0
[16] BiocParallel_1.20.0 matrixStats_0.55.0 Biobase_2.46.0
[19] GenomicRanges_1.38.0 GenomeInfoDb_1.22.0 IRanges_2.20.0
[22] S4Vectors_0.24.0 BiocGenerics_0.32.0 Cairo_1.5-10
[25] BiocStyle_2.14.0 OSCAUtils_0.0.1
loaded via a namespace (and not attached):
[1] Rtsne_0.15 ggbeeswarm_0.6.0 colorspace_1.4-1
[4] XVector_0.26.0 BiocNeighbors_1.4.0 bit64_0.9-7
[7] interactiveDisplayBase_1.24.0 codetools_0.2-16 knitr_1.26
[10] zeallot_0.1.0 Rsamtools_2.2.0 shiny_1.4.0
[13] BiocManager_1.30.9 compiler_3.6.1 httr_1.4.1
[16] dqrng_0.2.1 backports_1.1.5 assertthat_0.2.1
[19] Matrix_1.2-17 fastmap_1.0.1 lazyeval_0.2.2
[22] limma_3.42.0 later_1.0.0 BiocSingular_1.2.0
[25] htmltools_0.4.0 prettyunits_1.0.2 tools_3.6.1
[28] rsvd_1.0.2 igraph_1.2.4.1 gtable_0.3.0
[31] glue_1.3.1 GenomeInfoDbData_1.2.2 dplyr_0.8.3
[34] rappdirs_0.3.1 Rcpp_1.0.3 vctrs_0.2.0
[37] Biostrings_2.54.0 ExperimentHub_1.12.0 rtracklayer_1.46.0
[40] DelayedMatrixStats_1.8.0 xfun_0.11 stringr_1.4.0
[43] mime_0.7 irlba_2.3.3 statmod_1.4.32
[46] XML_3.98-1.20 edgeR_3.28.0 zlibbioc_1.32.0
[49] scales_1.0.0 hms_0.5.2 promises_1.1.0
[52] ProtGenerics_1.18.0 yaml_2.2.0 curl_4.2
[55] memoise_1.1.0 gridExtra_2.3 biomaRt_2.42.0
[58] stringi_1.4.3 RSQLite_2.1.2 BiocVersion_3.10.1
[61] rlang_0.4.1 pkgconfig_2.0.3 bitops_1.0-6
[64] evaluate_0.14 lattice_0.20-38 purrr_0.3.3
[67] labeling_0.3 GenomicAlignments_1.22.1 cowplot_1.0.0
[70] bit_1.1-14 tidyselect_0.2.5 magrittr_1.5
[73] bookdown_0.15 R6_2.4.1 DBI_1.0.0
[76] pillar_1.4.2 withr_2.1.2 RCurl_1.95-4.12
[79] tibble_2.1.3 crayon_1.3.4 rmarkdown_1.17
[82] viridis_0.5.1 progress_1.2.2 locfit_1.5-9.1
[85] grid_3.6.1 blob_1.2.0 digest_0.6.22
[88] xtable_1.8-4 httpuv_1.5.2 openssl_1.4.1
[91] munsell_0.5.0 beeswarm_0.2.3 viridisLite_0.3.0
[94] vipor_0.4.5 askpass_1.1 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.