Chapter 7 Lawlor human pancreas (SMARTer)
7.1 Introduction
This performs an analysis of the Lawlor et al. (2017) dataset, consisting of human pancreas cells from various donors.
7.3 Quality control
library(scater)
stats <- perCellQCMetrics(sce.lawlor,
subsets=list(Mito=which(rowData(sce.lawlor)$SEQNAME=="MT")))
qc <- quickPerCellQC(stats, percent_subsets="subsets_Mito_percent",
batch=sce.lawlor$`islet unos id`)
sce.lawlor <- sce.lawlor[,!qc$discard]colData(unfiltered) <- cbind(colData(unfiltered), stats)
unfiltered$discard <- qc$discard
gridExtra::grid.arrange(
plotColData(unfiltered, x="islet unos id", y="sum", colour_by="discard") +
scale_y_log10() + ggtitle("Total count") +
theme(axis.text.x = element_text(angle = 90)),
plotColData(unfiltered, x="islet unos id", y="detected",
colour_by="discard") + scale_y_log10() + ggtitle("Detected features") +
theme(axis.text.x = element_text(angle = 90)),
plotColData(unfiltered, x="islet unos id", y="subsets_Mito_percent",
colour_by="discard") + ggtitle("Mito percent") +
theme(axis.text.x = element_text(angle = 90)),
ncol=2
)
Figure 7.1: Distribution of each QC metric across cells from each donor of the Lawlor pancreas dataset. Each point represents a cell and is colored according to whether that cell was discarded.
Figure 7.2: Percentage of mitochondrial reads in each cell in the 416B dataset compared to the total count. Each point represents a cell and is colored according to whether that cell was discarded.
## low_lib_size low_n_features high_subsets_Mito_percent
## 9 5 25
## discard
## 347.4 Normalization
library(scran)
set.seed(1000)
clusters <- quickCluster(sce.lawlor)
sce.lawlor <- computeSumFactors(sce.lawlor, clusters=clusters)
sce.lawlor <- logNormCounts(sce.lawlor)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.295 0.781 0.963 1.000 1.182 2.629plot(librarySizeFactors(sce.lawlor), sizeFactors(sce.lawlor), pch=16,
xlab="Library size factors", ylab="Deconvolution factors", log="xy")
Figure 7.3: Relationship between the library size factors and the deconvolution size factors in the Lawlor pancreas dataset.
7.5 Variance modelling
Using age as a proxy for the donor.
dec.lawlor <- modelGeneVar(sce.lawlor, block=sce.lawlor$`islet unos id`)
chosen.genes <- getTopHVGs(dec.lawlor, n=2000)par(mfrow=c(4,2))
blocked.stats <- dec.lawlor$per.block
for (i in colnames(blocked.stats)) {
current <- blocked.stats[[i]]
plot(current$mean, current$total, main=i, pch=16, cex=0.5,
xlab="Mean of log-expression", ylab="Variance of log-expression")
curfit <- metadata(current)
curve(curfit$trend(x), col='dodgerblue', add=TRUE, lwd=2)
}
Figure 7.4: Per-gene variance as a function of the mean for the log-expression values in the Lawlor pancreas dataset. Each point represents a gene (black) with the mean-variance trend (blue) fitted separately for each donor.
7.7 Clustering
snn.gr <- buildSNNGraph(sce.lawlor, use.dimred="PCA")
colLabels(sce.lawlor) <- factor(igraph::cluster_walktrap(snn.gr)$membership)##
## Acinar Alpha Beta Delta Ductal Gamma/PP None/Other Stellate
## 1 1 0 0 13 2 16 2 0
## 2 0 1 76 1 0 0 0 0
## 3 0 161 1 0 0 1 2 0
## 4 0 1 0 1 0 0 5 19
## 5 0 0 175 4 1 0 1 0
## 6 22 0 0 0 0 0 0 0
## 7 0 75 0 0 0 0 0 0
## 8 0 0 0 1 20 0 2 0##
## ACCG268 ACCR015A ACEK420A ACEL337 ACHY057 ACIB065 ACIW009 ACJV399
## 1 8 2 2 4 4 4 9 1
## 2 14 3 2 33 3 2 4 17
## 3 36 23 14 13 14 14 21 30
## 4 7 1 0 1 0 4 9 4
## 5 34 10 4 39 7 23 24 40
## 6 0 2 13 0 0 0 5 2
## 7 32 12 0 5 6 7 4 9
## 8 1 1 2 1 2 1 12 3gridExtra::grid.arrange(
plotTSNE(sce.lawlor, colour_by="label"),
plotTSNE(sce.lawlor, colour_by="islet unos id"),
ncol=2
)
Figure 5.3: Obligatory \(t\)-SNE plots of the Lawlor 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] BiocSingular_1.16.0 scran_1.28.0
[3] scater_1.28.0 ggplot2_3.4.2
[5] scuttle_1.10.0 ensembldb_2.24.0
[7] AnnotationFilter_1.24.0 GenomicFeatures_1.52.0
[9] AnnotationDbi_1.62.0 AnnotationHub_3.8.0
[11] BiocFileCache_2.8.0 dbplyr_2.3.2
[13] scRNAseq_2.13.0 SingleCellExperiment_1.22.0
[15] SummarizedExperiment_1.30.0 Biobase_2.60.0
[17] GenomicRanges_1.52.0 GenomeInfoDb_1.36.0
[19] IRanges_2.34.0 S4Vectors_0.38.0
[21] BiocGenerics_0.46.0 MatrixGenerics_1.12.0
[23] matrixStats_0.63.0 BiocStyle_2.28.0
[25] rebook_1.10.0
loaded via a namespace (and not attached):
[1] jsonlite_1.8.4 CodeDepends_0.6.5
[3] magrittr_2.0.3 ggbeeswarm_0.7.1
[5] farver_2.1.1 rmarkdown_2.21
[7] BiocIO_1.10.0 zlibbioc_1.46.0
[9] vctrs_0.6.2 memoise_2.0.1
[11] Rsamtools_2.16.0 DelayedMatrixStats_1.22.0
[13] RCurl_1.98-1.12 htmltools_0.5.5
[15] progress_1.2.2 curl_5.0.0
[17] BiocNeighbors_1.18.0 sass_0.4.5
[19] bslib_0.4.2 cachem_1.0.7
[21] GenomicAlignments_1.36.0 igraph_1.4.2
[23] mime_0.12 lifecycle_1.0.3
[25] pkgconfig_2.0.3 rsvd_1.0.5
[27] Matrix_1.5-4 R6_2.5.1
[29] fastmap_1.1.1 GenomeInfoDbData_1.2.10
[31] shiny_1.7.4 digest_0.6.31
[33] colorspace_2.1-0 dqrng_0.3.0
[35] irlba_2.3.5.1 ExperimentHub_2.8.0
[37] RSQLite_2.3.1 beachmat_2.16.0
[39] labeling_0.4.2 filelock_1.0.2
[41] fansi_1.0.4 httr_1.4.5
[43] compiler_4.3.0 bit64_4.0.5
[45] withr_2.5.0 BiocParallel_1.34.0
[47] viridis_0.6.2 DBI_1.1.3
[49] highr_0.10 biomaRt_2.56.0
[51] rappdirs_0.3.3 DelayedArray_0.26.0
[53] bluster_1.10.0 rjson_0.2.21
[55] tools_4.3.0 vipor_0.4.5
[57] beeswarm_0.4.0 interactiveDisplayBase_1.38.0
[59] httpuv_1.6.9 glue_1.6.2
[61] restfulr_0.0.15 promises_1.2.0.1
[63] grid_4.3.0 Rtsne_0.16
[65] cluster_2.1.4 generics_0.1.3
[67] gtable_0.3.3 hms_1.1.3
[69] metapod_1.8.0 ScaledMatrix_1.8.0
[71] xml2_1.3.3 utf8_1.2.3
[73] XVector_0.40.0 ggrepel_0.9.3
[75] BiocVersion_3.17.1 pillar_1.9.0
[77] stringr_1.5.0 limma_3.56.0
[79] later_1.3.0 dplyr_1.1.2
[81] lattice_0.21-8 rtracklayer_1.60.0
[83] bit_4.0.5 tidyselect_1.2.0
[85] locfit_1.5-9.7 Biostrings_2.68.0
[87] knitr_1.42 gridExtra_2.3
[89] bookdown_0.33 ProtGenerics_1.32.0
[91] edgeR_3.42.0 xfun_0.39
[93] statmod_1.5.0 stringi_1.7.12
[95] lazyeval_0.2.2 yaml_2.3.7
[97] evaluate_0.20 codetools_0.2-19
[99] tibble_3.2.1 BiocManager_1.30.20
[101] graph_1.78.0 cli_3.6.1
[103] xtable_1.8-4 munsell_0.5.0
[105] jquerylib_0.1.4 Rcpp_1.0.10
[107] dir.expiry_1.8.0 png_0.1-8
[109] XML_3.99-0.14 parallel_4.3.0
[111] ellipsis_0.3.2 blob_1.2.4
[113] prettyunits_1.1.1 sparseMatrixStats_1.12.0
[115] bitops_1.0-7 viridisLite_0.4.1
[117] scales_1.2.1 purrr_1.0.1
[119] crayon_1.5.2 rlang_1.1.0
[121] cowplot_1.1.1 KEGGREST_1.40.0 References
Lawlor, N., J. George, M. Bolisetty, R. Kursawe, L. Sun, V. Sivakamasundari, I. Kycia, P. Robson, and M. L. Stitzel. 2017. “Single-cell transcriptomes identify human islet cell signatures and reveal cell-type-specific expression changes in type 2 diabetes.” Genome Res. 27 (2): 208–22.