Cell type composition analysis
Belinda Phipson
10/02/2021
Last updated: 2021-02-10
Checks: 6 1
Knit directory: Human_Development_snRNAseq/
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Introduction
Here we test whether the cell type composition of the heart, young and adult samples differ using the propeller function in the speckle package.
Load libraries and functions
library(edgeR)
library(RColorBrewer)
library(org.Hs.eg.db)
library(limma)
library(Seurat)
library(monocle)
library(cowplot)
library(DelayedArray)
library(scran)
library(NMF)
library(workflowr)
library(ggplot2)
library(clustree)
library(dplyr)
library(speckle)Load the heart data
heart <- readRDS(file="./output/heart-int-FYA.Rds")Set up cell information for propeller analysis
heart$sample <- factor(heart$biorep, levels=c(paste("f",1:3, sep=""),
paste("y",1:3, sep=""),
paste("a",1:3, sep="")))
heart$group <- NA
heart$group[grep("f",heart$sample)] <- "fetal"
heart$group[grep("y",heart$sample)] <- "young"
heart$group[grep("a",heart$sample)] <- "adult"
heart$group <- factor(heart$group, levels=c("fetal", "young","adult"))Visualise the data
tSNE plots
DimPlot(heart, reduction="tsne", group.by = "Broad_celltype")
DimPlot(heart, reduction="tsne", group.by = "Broad_celltype", split.by="group")
Barplots of proportions
plotCellTypeProps(clusters=heart$Broad_celltype, sample=heart$sample)
Biological variability plots
These plots show that there is a massive amount of biological variability between the samples, hence using Poisson or binomial models are not appropriate.
# get the cell type counts and proportions
x <- getTransformedProps(clusters = heart$Broad_celltype, sample=heart$sample,
transform="logit")
par(mfrow=c(1,2))
plotCellTypeMeanVar(x$Counts)Design matrix not provided. Switch to the classic mode.plotCellTypePropsMeanVar(x$Counts)
Testing for differences in proportions
Idents(heart) <- heart$Broad_celltypeout <- propeller(heart, transform = "logit")
out BaselineProp PropMean.fetal PropMean.young PropMean.adult
Erythroid 0.002271888 0.004433044 0.00000000 0.000000000
Immune cells 0.076302180 0.027545963 0.10875124 0.189587828
Cardiomyocytes 0.549464352 0.682410381 0.42676145 0.273546585
Fibroblast 0.182101958 0.111342233 0.26192406 0.298689329
Neurons 0.016143332 0.012643346 0.02620977 0.011380837
Epicardial cells 0.064166975 0.051414853 0.07541028 0.093157709
Smooth muscle cells 0.007942372 0.008101973 0.00846540 0.009099294
Endothelial cells 0.101606945 0.102108207 0.09247781 0.124538418
Fstatistic P.Value FDR
Erythroid 46.5919981 5.825757e-21 4.660605e-20
Immune cells 11.5750929 9.397256e-06 3.758902e-05
Cardiomyocytes 9.0682295 1.152704e-04 3.073879e-04
Fibroblast 4.6307938 9.747019e-03 1.949404e-02
Neurons 1.5192452 2.188770e-01 3.502033e-01
Epicardial cells 0.9097871 4.026099e-01 5.368133e-01
Smooth muscle cells 0.4216918 6.559362e-01 7.117040e-01
Endothelial cells 0.3400932 7.117040e-01 7.117040e-01# Significant cell types at FDR 0.05
rownames(out)[which(out$FDR<0.05)][1] "Erythroid" "Immune cells" "Cardiomyocytes" "Fibroblast" Visualise the results
# Set up group information based on counts matrix/matrix of proportions
group <- factor(rep(c("fetal","young","adult"), each=3),
levels=c("fetal","young","adult"))ct <- rownames(out)
par(mfrow=c(3,3))
for(i in 1:nrow(out)){
stripchart(x$Proportions[ct[i],]~group, vertical=TRUE, pch=16,
method="jitter", ylab="Proportion", main=ct[i],
col=ggplotColors(3), cex=1.5, cex.lab=1.5, cex.axis=1.5,
cex.main=1.5)
}
sessionInfo()R version 4.0.2 (2020-06-22)
Platform: x86_64-apple-darwin17.0 (64-bit)
Running under: macOS Catalina 10.15.7
Matrix products: default
BLAS: /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRblas.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRlapack.dylib
locale:
[1] en_AU.UTF-8/en_AU.UTF-8/en_AU.UTF-8/C/en_AU.UTF-8/en_AU.UTF-8
attached base packages:
[1] splines parallel stats4 stats graphics grDevices utils
[8] datasets methods base
other attached packages:
[1] speckle_0.0.2 dplyr_1.0.2
[3] clustree_0.4.3 ggraph_2.0.4
[5] NMF_0.23.0 cluster_2.1.0
[7] rngtools_1.5 pkgmaker_0.32.2
[9] registry_0.5-1 scran_1.18.1
[11] SingleCellExperiment_1.12.0 SummarizedExperiment_1.20.0
[13] GenomicRanges_1.42.0 GenomeInfoDb_1.26.1
[15] DelayedArray_0.16.0 MatrixGenerics_1.2.0
[17] matrixStats_0.57.0 cowplot_1.1.0
[19] monocle_2.18.0 DDRTree_0.1.5
[21] irlba_2.3.3 VGAM_1.1-4
[23] ggplot2_3.3.2 Matrix_1.2-18
[25] Seurat_3.2.2 org.Hs.eg.db_3.12.0
[27] AnnotationDbi_1.52.0 IRanges_2.24.0
[29] S4Vectors_0.28.0 Biobase_2.50.0
[31] BiocGenerics_0.36.0 RColorBrewer_1.1-2
[33] edgeR_3.32.0 limma_3.46.0
[35] workflowr_1.6.2
loaded via a namespace (and not attached):
[1] reticulate_1.18 tidyselect_1.1.0
[3] RSQLite_2.2.1 htmlwidgets_1.5.2
[5] grid_4.0.2 combinat_0.0-8
[7] docopt_0.7.1 BiocParallel_1.24.1
[9] Rtsne_0.15 munsell_0.5.0
[11] codetools_0.2-18 ica_1.0-2
[13] statmod_1.4.35 future_1.20.1
[15] miniUI_0.1.1.1 withr_2.3.0
[17] colorspace_2.0-0 fastICA_1.2-2
[19] knitr_1.30 rstudioapi_0.13
[21] ROCR_1.0-11 tensor_1.5
[23] listenv_0.8.0 labeling_0.4.2
[25] git2r_0.27.1 slam_0.1-47
[27] GenomeInfoDbData_1.2.4 polyclip_1.10-0
[29] farver_2.0.3 bit64_4.0.5
[31] pheatmap_1.0.12 rprojroot_2.0.2
[33] parallelly_1.21.0 vctrs_0.3.5
[35] generics_0.1.0 xfun_0.19
[37] R6_2.5.0 doParallel_1.0.16
[39] graphlayouts_0.7.1 rsvd_1.0.3
[41] locfit_1.5-9.4 bitops_1.0-6
[43] spatstat.utils_1.17-0 assertthat_0.2.1
[45] promises_1.1.1 scales_1.1.1
[47] gtable_0.3.0 beachmat_2.6.2
[49] globals_0.14.0 goftest_1.2-2
[51] tidygraph_1.2.0 rlang_0.4.9
[53] lazyeval_0.2.2 yaml_2.2.1
[55] reshape2_1.4.4 abind_1.4-5
[57] httpuv_1.5.4 tools_4.0.2
[59] gridBase_0.4-7 ellipsis_0.3.1
[61] ggridges_0.5.2 Rcpp_1.0.5
[63] plyr_1.8.6 sparseMatrixStats_1.2.0
[65] zlibbioc_1.36.0 purrr_0.3.4
[67] RCurl_1.98-1.2 densityClust_0.3
[69] rpart_4.1-15 deldir_0.2-3
[71] pbapply_1.4-3 viridis_0.5.1
[73] zoo_1.8-8 ggrepel_0.8.2
[75] fs_1.5.0 magrittr_2.0.1
[77] data.table_1.13.2 lmtest_0.9-38
[79] RANN_2.6.1 fitdistrplus_1.1-1
[81] patchwork_1.1.0 mime_0.9
[83] evaluate_0.14 xtable_1.8-4
[85] sparsesvd_0.2 gridExtra_2.3
[87] HSMMSingleCell_1.10.0 compiler_4.0.2
[89] tibble_3.0.4 KernSmooth_2.23-18
[91] crayon_1.3.4 htmltools_0.5.0
[93] mgcv_1.8-33 later_1.1.0.1
[95] tidyr_1.1.2 DBI_1.1.0
[97] tweenr_1.0.1 MASS_7.3-53
[99] igraph_1.2.6 pkgconfig_2.0.3
[101] plotly_4.9.2.1 scuttle_1.0.3
[103] foreach_1.5.1 dqrng_0.2.1
[105] XVector_0.30.0 stringr_1.4.0
[107] digest_0.6.27 sctransform_0.3.1
[109] RcppAnnoy_0.0.17 spatstat.data_1.5-2
[111] rmarkdown_2.5 leiden_0.3.5
[113] uwot_0.1.9 DelayedMatrixStats_1.12.1
[115] shiny_1.5.0 lifecycle_0.2.0
[117] nlme_3.1-150 jsonlite_1.7.1
[119] BiocNeighbors_1.8.1 viridisLite_0.3.0
[121] pillar_1.4.7 lattice_0.20-41
[123] fastmap_1.0.1 httr_1.4.2
[125] survival_3.2-7 glue_1.4.2
[127] qlcMatrix_0.9.7 FNN_1.1.3
[129] spatstat_1.64-1 png_0.1-7
[131] iterators_1.0.13 bluster_1.0.0
[133] bit_4.0.4 ggforce_0.3.2
[135] stringi_1.5.3 blob_1.2.1
[137] BiocSingular_1.6.0 memoise_1.1.0
[139] future.apply_1.6.0