Marker analysis of broad cell types
Belinda Phipson
10/02/2021
Last updated: 2021-02-10
Checks: 6 1
Knit directory: Human_Development_snRNAseq/
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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(gridBase)
library(grid)
library(speckle)Read in the data objects
targets <- read.delim("./data/targets.txt",header=TRUE, stringsAsFactors = FALSE)
targets$FileName2 <- paste(targets$FileName,"/",sep="")
targets$Group_ID2 <- gsub("LV_","",targets$Group_ID)
group <- c("Fetal_1","Fetal_2","Fetal_3",
"Young_1","Young_2","Young_3",
"Adult_1","Adult_2","Adult_3",
"Diseased_1","Diseased_2",
"Diseased_3","Diseased_4")
m <- match(group, targets$Group_ID2)
targets <- targets[m,]# Load unfiltered counts matrix for every sample (object all)
load("./output/all-counts.Rdata")heart <- readRDS(file="./output/heartFYA.Rds")heart$Broad_celltype <- factor(heart$Broad_celltype,
levels=c("Cardiomyocytes","Fibroblast","Endothelial cells",
"Immune cells","Epicardial cells","Neurons",
"Smooth muscle cells","Erythroid"))
heart$biorep <- factor(heart$biorep,levels=c("f1","f2","f3","y1","y2","y3","a1","a2","a3"))
table(heart$biorep,heart$Broad_celltype)
all.bct <- factor(heart$Broad_celltype,
levels=c("Cardiomyocytes","Fibroblast","Endothelial cells",
"Immune cells","Epicardial cells","Neurons",
"Smooth muscle cells","Erythroid"))
sample <- heart$biorepm <- match(colnames(heart),colnames(all))
all.counts <- all[,m]Get gene annotation and perform filtering
columns(org.Hs.eg.db)
ann <- AnnotationDbi:::select(org.Hs.eg.db,keys=rownames(all.counts),columns=c("SYMBOL","ENTREZID","ENSEMBL","GENENAME","CHR"),keytype = "SYMBOL")
m <- match(rownames(all.counts),ann$SYMBOL)
ann <- ann[m,]mito <- grep("mitochondrial",ann$GENENAME)
ribo <- grep("ribosomal",ann$GENENAME)
missingEZID <- which(is.na(ann$ENTREZID))chuck <- unique(c(mito,ribo,missingEZID))
all.counts.keep <- all.counts[-chuck,]
ann.keep <- ann[-chuck,]numzero.genes <- rowSums(all.counts.keep==0)
keep.genes <- numzero.genes < (ncol(all.counts.keep)-20)
all.keep <- all.counts.keep[keep.genes,]
ann.keep.all <- ann.keep[keep.genes,]Limma analysis
logcounts.all <- normCounts(all.keep,log=TRUE,prior.count=0.5)
design <- model.matrix(~0+all.bct+sample)
colnames(design)[1:(length(levels(all.bct)))] <- levels(all.bct)
mycont <- matrix(0,ncol=length(levels(all.bct)),nrow=length(levels(all.bct)))
colnames(mycont)<-levels(all.bct)
diag(mycont)<-1
mycont[upper.tri(mycont)]<- -1/(length(levels(all.bct))-1)
mycont[lower.tri(mycont)]<- -1/(length(levels(all.bct))-1)
# Fill out remaining rows with 0s
zero.rows <- matrix(0,ncol=length(levels(all.bct)),nrow=(ncol(design)-length(levels(all.bct))))
test <- rbind(mycont,zero.rows)
fit <- lmFit(logcounts.all,design)
fit.cont <- contrasts.fit(fit,contrasts=test)
fit.cont <- eBayes(fit.cont,trend=TRUE,robust=TRUE)
fit.cont$genes <- ann.keep.all
treat.all <- treat(fit.cont,lfc=0.5)dt <- decideTests(treat.all)
summary(dt) Cardiomyocytes Fibroblast Endothelial cells Immune cells
Down 323 207 418 610
NotSig 18056 18314 18407 17783
Up 822 680 376 808
Epicardial cells Neurons Smooth muscle cells Erythroid
Down 125 261 79 1493
NotSig 18512 18392 18627 17340
Up 564 548 495 368par(mfrow=c(3,3))
par(mar=c(5,5,2,2))
for(i in 1:ncol(treat.all)){
plotMD(treat.all,coef=i,status = dt[,i],hl.cex=0.5)
abline(h=0,col=colours()[c(226)])
lines(lowess(treat.all$Amean,treat.all$coefficients[,i]),lwd=1.5,col=4)
}
DotPlot to visualise marker genes
heart <- readRDS("./output/heartFYA.Rds")DefaultAssay(heart) <- "RNA"
sig.genes <- gene.label <- vector("list", ncol(treat.all))
for(i in 1:length(sig.genes)){
top <- topTreat(treat.all,coef=i,n=Inf,sort.by="t")
sig.genes[[i]] <- rownames(top)[top$logFC>0][1:10]
gene.label[[i]] <- paste(rownames(top)[top$logFC>0][1:10],colnames(treat.all)[i],sep="-")
}
csig <- unlist(sig.genes)
genes <- unlist(gene.label)
missing <- is.na(match(csig,rownames(heart)))
csig2 <- csig[!missing]
gene.cols <- rep(c(ggplotColors(7),"grey"),each=10)
gene.cols <- gene.cols[!missing]
d <- duplicated(csig2)
csig2 <- csig2[!d]
gene.cols <- gene.cols[!d]DotPlot(heart,features=unique(csig2),group.by="Broad_celltype",cols = c("lightgrey", "red"))+RotatedAxis() + FontSize(y.text = 8, x.text=14) + labs(y=element_blank(),x=element_blank()) + coord_flip() + theme(axis.text.y = element_text(color=(gene.cols)))
Perform gene set testing on reactome sets
load("./output/human_c2_v5p2.rdata")
c2.id <- ids2indices(Hs.c2,treat.all$genes$ENTREZID)
reactome.id <-c2.id[grep("REACTOME",names(c2.id))]Reactome figures - cardio
cardio.camera <- cameraPR(treat.all$t[,1],reactome.id)
cardio.camera.up <- cardio.camera[cardio.camera[,2]=="Up",]
fibro.camera <- cameraPR(treat.all$t[,2],reactome.id)
fibro.camera.up <- fibro.camera[fibro.camera[,2]=="Up",]
endo.camera <- cameraPR(treat.all$t[,3],reactome.id)
endo.camera.up <- endo.camera[endo.camera[,2]=="Up",]
immune.camera <- cameraPR(treat.all$t[,4],reactome.id)
immune.camera.up <- immune.camera[immune.camera[,2]=="Up",]
epic.camera <- cameraPR(treat.all$t[,5],reactome.id)
epic.camera.up <- epic.camera[epic.camera[,2]=="Up",]
neuron.camera <- cameraPR(treat.all$t[,6],reactome.id)
neuron.camera.up <- neuron.camera[neuron.camera[,2]=="Up",]
smc.camera <- cameraPR(treat.all$t[,7],reactome.id)
smc.camera.up <- smc.camera[smc.camera[,2]=="Up",]
eryth.camera <- cameraPR(treat.all$t[,8],reactome.id)
eryth.camera.up <- eryth.camera[eryth.camera[,2]=="Up",]
nsets <- 5
all.cam <- rbind(cardio.camera.up[1:nsets,], fibro.camera.up[1:nsets,],
endo.camera.up[1:nsets,],immune.camera.up[1:nsets,],
epic.camera.up[1:nsets,],neuron.camera.up[1:nsets,],
smc.camera.up[1:nsets,],eryth.camera.up[1:nsets,])
scores <- -log10(all.cam$PValue)
names(scores) <- rownames(all.cam)
names(scores) <- gsub("REACTOME_","",names(scores))par(mfrow=c(1,1))
par(mar=c(5,41,3,2))
barplot(scores[length(scores):1],horiz = T,las=2,col=rev(rep(c(ggplotColors(7),"grey"),each=nsets)),cex.names=0.9,
cex.axis = 1.5,xlab="-log10(PValue)",cex.lab=1.5)
abline(v= -log10(0.05),lty=2)
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] grid splines parallel stats4 stats graphics grDevices
[8] utils datasets methods base
other attached packages:
[1] speckle_0.0.2 gridBase_0.4-7
[3] dplyr_1.0.2 clustree_0.4.3
[5] ggraph_2.0.4 NMF_0.23.0
[7] cluster_2.1.0 rngtools_1.5
[9] pkgmaker_0.32.2 registry_0.5-1
[11] scran_1.18.1 SingleCellExperiment_1.12.0
[13] SummarizedExperiment_1.20.0 GenomicRanges_1.42.0
[15] GenomeInfoDb_1.26.1 DelayedArray_0.16.0
[17] MatrixGenerics_1.2.0 matrixStats_0.57.0
[19] cowplot_1.1.0 monocle_2.18.0
[21] DDRTree_0.1.5 irlba_2.3.3
[23] VGAM_1.1-4 ggplot2_3.3.2
[25] Matrix_1.2-18 Seurat_3.2.2
[27] org.Hs.eg.db_3.12.0 AnnotationDbi_1.52.0
[29] IRanges_2.24.0 S4Vectors_0.28.0
[31] Biobase_2.50.0 BiocGenerics_0.36.0
[33] RColorBrewer_1.1-2 edgeR_3.32.0
[35] limma_3.46.0 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] combinat_0.0-8 docopt_0.7.1
[7] BiocParallel_1.24.1 Rtsne_0.15
[9] munsell_0.5.0 codetools_0.2-18
[11] ica_1.0-2 statmod_1.4.35
[13] future_1.20.1 miniUI_0.1.1.1
[15] withr_2.3.0 colorspace_2.0-0
[17] fastICA_1.2-2 knitr_1.30
[19] rstudioapi_0.13 ROCR_1.0-11
[21] tensor_1.5 listenv_0.8.0
[23] labeling_0.4.2 git2r_0.27.1
[25] slam_0.1-47 GenomeInfoDbData_1.2.4
[27] polyclip_1.10-0 farver_2.0.3
[29] bit64_4.0.5 pheatmap_1.0.12
[31] rprojroot_2.0.2 parallelly_1.21.0
[33] vctrs_0.3.5 generics_0.1.0
[35] xfun_0.19 R6_2.5.0
[37] doParallel_1.0.16 graphlayouts_0.7.1
[39] rsvd_1.0.3 locfit_1.5-9.4
[41] bitops_1.0-6 spatstat.utils_1.17-0
[43] assertthat_0.2.1 promises_1.1.1
[45] scales_1.1.1 gtable_0.3.0
[47] beachmat_2.6.2 globals_0.14.0
[49] goftest_1.2-2 tidygraph_1.2.0
[51] rlang_0.4.9 lazyeval_0.2.2
[53] yaml_2.2.1 reshape2_1.4.4
[55] abind_1.4-5 httpuv_1.5.4
[57] tools_4.0.2 ellipsis_0.3.1
[59] ggridges_0.5.2 Rcpp_1.0.5
[61] plyr_1.8.6 sparseMatrixStats_1.2.0
[63] zlibbioc_1.36.0 purrr_0.3.4
[65] RCurl_1.98-1.2 densityClust_0.3
[67] rpart_4.1-15 deldir_0.2-3
[69] pbapply_1.4-3 viridis_0.5.1
[71] zoo_1.8-8 ggrepel_0.8.2
[73] fs_1.5.0 magrittr_2.0.1
[75] data.table_1.13.2 lmtest_0.9-38
[77] RANN_2.6.1 fitdistrplus_1.1-1
[79] patchwork_1.1.0 mime_0.9
[81] evaluate_0.14 xtable_1.8-4
[83] sparsesvd_0.2 gridExtra_2.3
[85] HSMMSingleCell_1.10.0 compiler_4.0.2
[87] tibble_3.0.4 KernSmooth_2.23-18
[89] crayon_1.3.4 htmltools_0.5.0
[91] mgcv_1.8-33 later_1.1.0.1
[93] tidyr_1.1.2 DBI_1.1.0
[95] tweenr_1.0.1 MASS_7.3-53
[97] igraph_1.2.6 pkgconfig_2.0.3
[99] plotly_4.9.2.1 scuttle_1.0.3
[101] foreach_1.5.1 dqrng_0.2.1
[103] XVector_0.30.0 stringr_1.4.0
[105] digest_0.6.27 sctransform_0.3.1
[107] RcppAnnoy_0.0.17 spatstat.data_1.5-2
[109] rmarkdown_2.5 leiden_0.3.5
[111] uwot_0.1.9 DelayedMatrixStats_1.12.1
[113] shiny_1.5.0 lifecycle_0.2.0
[115] nlme_3.1-150 jsonlite_1.7.1
[117] BiocNeighbors_1.8.1 viridisLite_0.3.0
[119] pillar_1.4.7 lattice_0.20-41
[121] fastmap_1.0.1 httr_1.4.2
[123] survival_3.2-7 glue_1.4.2
[125] qlcMatrix_0.9.7 FNN_1.1.3
[127] spatstat_1.64-1 png_0.1-7
[129] iterators_1.0.13 bluster_1.0.0
[131] bit_4.0.4 ggforce_0.3.2
[133] stringi_1.5.3 blob_1.2.1
[135] BiocSingular_1.6.0 memoise_1.1.0
[137] future.apply_1.6.0