Recluster epicardial lineage
Belinda Phipson
8/15/2019
Last updated: 2019-10-28
Checks: 6 0
Knit directory: Porello-heart-snRNAseq/
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| Rmd | a1cc833 | Belinda Phipson | 2019-10-28 | recluster epicardial lineage |
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)source("/misc/card2-single_cell_nuclei_rnaseq/Porello-heart-snRNAseq/code/normCounts.R")
source("/misc/card2-single_cell_nuclei_rnaseq/Porello-heart-snRNAseq/code/findModes.R")
source("/misc/card2-single_cell_nuclei_rnaseq/Porello-heart-snRNAseq/code/ggplotColors.R")targets <- read.delim("/misc/card2-single_cell_nuclei_rnaseq/Porello-heart-snRNAseq/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$Group_ID2)
targets <- targets[m,]fetal.integrated <- readRDS(file="./output/RDataObjects/fetal-int.Rds")
load(file="./output/RDataObjects/fetalObjs.Rdata")
young.integrated <- readRDS(file="./output/RDataObjects/young-int.Rds")
load(file="./output/RDataObjects/youngObjs.Rdata")
adult.integrated <- readRDS(file="./output/RDataObjects/adult-int.Rds")
load(file="./output/RDataObjects/adultObjs.Rdata")Set default clustering resolution
# Default 0.3
Idents(fetal.integrated) <- fetal.integrated$integrated_snn_res.0.3
DimPlot(fetal.integrated, reduction = "tsne",label=TRUE,label.size = 6)+NoLegend()
# Default 0.3
DimPlot(young.integrated, reduction = "tsne",label=TRUE,label.size = 6)+NoLegend()
# Default 0.6
DimPlot(adult.integrated, reduction = "tsne",label=TRUE,label.size = 6)+NoLegend()
Merge all data together
heart <- merge(fetal.integrated, y = c(young.integrated, adult.integrated), project = "heart")
table(heart$orig.ident)
adult fetal young
9416 27760 16964 DefaultAssay(object = heart) <- "RNA"Get epicardial cells only
epic <- subset(heart,subset = Broad_celltype == "Epicardial cells")Filter out crappy cells
Check for cells with very low number of uniquely detected genes.
par(mfrow=c(1,2))
plot(density(epic$nFeature_RNA),main="Number of genes detected")
abline(v=500,col=2)
plot(density(epic$nCount_RNA),main="Library size")
abline(v=2500,col=2)
#epic <- subset(epic, subset = nFeature_RNA > 500 & nCount_RNA > 2500)
dim(epic)[1] 17926 3474table(epic$biorep)
a1 a2 a3 f1 f2 f3 y1 y2 y3
343 493 92 564 425 404 613 280 260 Run new integration with SCtransform normalisation
epic.list <- SplitObject(epic, split.by = "biorep")for (i in 1:length(epic.list)) {
epic.list[[i]] <- SCTransform(epic.list[[i]], verbose = FALSE)
}min(sapply(epic.list, ncol))[1] 92epic.anchors <- FindIntegrationAnchors(object.list = epic.list, dims=1:30,anchor.features = 3000,k.filter=92)
epic.integrated <- IntegrateData(anchorset = epic.anchors,dims=1:30)Perform clustering
DefaultAssay(object = epic.integrated) <- "integrated"Perform scaling and PCA
epic.integrated <- ScaleData(epic.integrated, verbose = FALSE)
epic.integrated <- RunPCA(epic.integrated, npcs = 50, verbose = FALSE)
ElbowPlot(epic.integrated,ndims=50)
VizDimLoadings(epic.integrated, dims = 1:4, reduction = "pca")
DimPlot(epic.integrated, reduction = "pca",group.by="orig.ident")
DimPlot(epic.integrated, reduction = "pca",group.by="biorep")
DimPlot(epic.integrated, reduction = "pca",group.by="sex")
DimPlot(epic.integrated, reduction = "pca",group.by="batch")
DimHeatmap(epic.integrated, dims = 1:15, cells = 500, balanced = TRUE)
DimHeatmap(epic.integrated, dims = 16:30, cells = 500, balanced = TRUE)
DimHeatmap(epic.integrated, dims = 31:45, cells = 500, balanced = TRUE)
Perform nearest neighbours clustering
epic.integrated <- FindNeighbors(epic.integrated, dims = 1:20)
epic.integrated <- FindClusters(epic.integrated, resolution = 0.1)Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 3474
Number of edges: 158223
Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.9202
Number of communities: 4
Elapsed time: 0 secondstable(Idents(epic.integrated))
0 1 2 3
2370 832 192 80 par(mar=c(5,4,2,2))
barplot(table(Idents(epic.integrated)),ylab="Number of cells",xlab="Clusters")
title("Number of cells in each cluster")
Visualisation with TSNE
set.seed(10)
epic.integrated <- RunTSNE(epic.integrated, reduction = "pca", dims = 1:20)DimPlot(epic.integrated, reduction = "tsne",label=TRUE,label.size = 6,pt.size = 0.5)+NoLegend()
pdf(file="./output/Figures/tsne-epicALL-res01.pdf",width=10,height=8,onefile = FALSE)
DimPlot(epic.integrated, reduction = "tsne",label=TRUE,label.size = 6,pt.size = 0.5)+NoLegend()
dev.off()png
2 DimPlot(epic.integrated, reduction = "tsne", group.by = "orig.ident")
DimPlot(epic.integrated, reduction = "tsne", split.by = "orig.ident")
DimPlot(epic.integrated, reduction = "tsne", group.by = "biorep")
DimPlot(epic.integrated, reduction = "tsne", group.by = "sex")
DimPlot(epic.integrated, reduction = "tsne", split.by = "sex")
DimPlot(epic.integrated, reduction = "tsne", group.by = "batch")
par(mfrow=c(1,1))
par(mar=c(4,4,2,2))
tab <- table(Idents(epic.integrated),epic.integrated$biorep)
barplot(t(tab/rowSums(tab)),beside=TRUE,col=ggplotColors(9),legend=TRUE)
par(mfrow=c(1,1))
par(mar=c(4,4,2,2))
tab <- table(Idents(epic.integrated),epic.integrated$orig.ident)
barplot(t(tab/rowSums(tab)),beside=TRUE,col=ggplotColors(3))
legend("topleft",legend=colnames(tab),fill=ggplotColors(3))
Visualisation with clustree
clusres <- c(0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9,1,1.1,1.2)for(i in 1:length(clusres)){
epic.integrated <- FindClusters(epic.integrated,
resolution = clusres[i])
}Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 3474
Number of edges: 158223
Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.9202
Number of communities: 4
Elapsed time: 0 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 3474
Number of edges: 158223
Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.8777
Number of communities: 6
Elapsed time: 0 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 3474
Number of edges: 158223
Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.8464
Number of communities: 7
Elapsed time: 0 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 3474
Number of edges: 158223
Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.8172
Number of communities: 8
Elapsed time: 0 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 3474
Number of edges: 158223
Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.7924
Number of communities: 9
Elapsed time: 0 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 3474
Number of edges: 158223
Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.7687
Number of communities: 9
Elapsed time: 0 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 3474
Number of edges: 158223
Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.7466
Number of communities: 9
Elapsed time: 0 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 3474
Number of edges: 158223
Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.7319
Number of communities: 11
Elapsed time: 0 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 3474
Number of edges: 158223
Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.7188
Number of communities: 11
Elapsed time: 0 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 3474
Number of edges: 158223
Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.7059
Number of communities: 12
Elapsed time: 0 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 3474
Number of edges: 158223
Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.6935
Number of communities: 13
Elapsed time: 0 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 3474
Number of edges: 158223
Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.6836
Number of communities: 13
Elapsed time: 0 secondspct.male <- function(x) {mean(x=="m")}
pct.female <- function(x) {mean(x=="f")}
pct.fetal <- function(x) {mean(x=="fetal")}
pct.young <- function(x) {mean(x=="young")}
pct.adult <- function(x) {mean(x=="adult")}clustree(epic.integrated, prefix = "integrated_snn_res.")
clustree(epic.integrated, prefix = "integrated_snn_res.",
node_colour = "sex", node_colour_aggr = "pct.female",assay="RNA")
clustree(epic.integrated, prefix = "integrated_snn_res.",
node_colour = "sex", node_colour_aggr = "pct.male",assay="RNA")
clustree(epic.integrated, prefix = "integrated_snn_res.",
node_colour = "orig.ident", node_colour_aggr = "pct.fetal",assay="RNA")
clustree(epic.integrated, prefix = "integrated_snn_res.",
node_colour = "orig.ident", node_colour_aggr = "pct.young",assay="RNA")
clustree(epic.integrated, prefix = "integrated_snn_res.",
node_colour = "orig.ident", node_colour_aggr = "pct.adult",assay="RNA")
Save Seurat object
DefaultAssay(epic.integrated) <- "RNA"
Idents(epic.integrated) <- epic.integrated$integrated_snn_res.0.1saveRDS(epic.integrated,file="./output/RDataObjects/epic-int-FYA-filtered.Rds")
#epic.integrated <- readRDS(file="./output/RDataObjects/epic-int-FYA.Rds")
# Load unfiltered counts matrix for every sample (object all)
load("./output/RDataObjects/all-counts.Rdata")columns(org.Hs.eg.db) [1] "ACCNUM" "ALIAS" "ENSEMBL" "ENSEMBLPROT"
[5] "ENSEMBLTRANS" "ENTREZID" "ENZYME" "EVIDENCE"
[9] "EVIDENCEALL" "GENENAME" "GO" "GOALL"
[13] "IPI" "MAP" "OMIM" "ONTOLOGY"
[17] "ONTOLOGYALL" "PATH" "PFAM" "PMID"
[21] "PROSITE" "REFSEQ" "SYMBOL" "UCSCKG"
[25] "UNIGENE" "UNIPROT" ann <- AnnotationDbi:::select(org.Hs.eg.db,keys=rownames(all),columns=c("SYMBOL","ENTREZID","ENSEMBL","GENENAME","CHR"),keytype = "SYMBOL")
m <- match(rownames(all),ann$SYMBOL)
ann <- ann[m,]
table(ann$SYMBOL==rownames(all))
TRUE
33939 mito <- grep("mitochondrial",ann$GENENAME)
length(mito)[1] 226ribo <- grep("ribosomal",ann$GENENAME)
length(ribo)[1] 198missingEZID <- which(is.na(ann$ENTREZID))
length(missingEZID)[1] 10530Find Markers
# Limma-trend for DE
m <- match(colnames(epic.integrated),colnames(all))
all.counts <- all[,m]chuck <- unique(c(mito,ribo,missingEZID))
length(chuck)[1] 10875all.counts.keep <- all.counts[-chuck,]
ann.keep <- ann[-chuck,]
table(ann.keep$SYMBOL==rownames(all.counts.keep))
TRUE
23064 numzero.genes <- rowSums(all.counts.keep==0)
#avg.exp <- rowMeans(cpm.DGEList(y.kid,log=TRUE))
#plot(avg.exp,numzero.genes,xlab="Average log-normalised-counts",ylab="Number zeroes per gene")
table(numzero.genes > (ncol(all.counts.keep)-20))
FALSE TRUE
15402 7662 keep.genes <- numzero.genes < (ncol(all.counts.keep)-20)
table(keep.genes)keep.genes
FALSE TRUE
7760 15304 all.keep <- all.counts.keep[keep.genes,]
dim(all.keep)[1] 15304 3474ann.keep <- ann.keep[keep.genes,]y.epic <- DGEList(all.keep)
logcounts <- normCounts(y.epic,log=TRUE,prior.count=0.5)
#logcounts.n <- normalizeBetweenArrays(logcounts, method = "cyclicloess")
maxclust <- length(levels(Idents(epic.integrated)))-1
grp <- paste("c",Idents(epic.integrated),sep = "")
grp <- factor(grp,levels = paste("c",0:maxclust,sep=""))
design <- model.matrix(~0+grp+epic.integrated$biorep)
colnames(design)[1:(maxclust+1)] <- levels(grp)
mycont <- matrix(0,ncol=length(levels(grp)),nrow=length(levels(grp)))
colnames(mycont)<-levels(grp)
diag(mycont)<-1
mycont[upper.tri(mycont)]<- -1/(length(levels(factor(grp)))-1)
mycont[lower.tri(mycont)]<- -1/(length(levels(factor(grp)))-1)
# Fill out remaining rows with 0s
zero.rows <- matrix(0,ncol=length(levels(grp)),nrow=(ncol(design)-length(levels(Idents(epic.integrated)))))
test <- rbind(mycont,zero.rows)
fit <- lmFit(logcounts,design)
fit.cont <- contrasts.fit(fit,contrasts=test)
fit.cont <- eBayes(fit.cont,trend=TRUE,robust=TRUE)
fit.cont$genes <- ann.keep
summary(decideTests(fit.cont)) c0 c1 c2 c3
Down 2609 1572 1526 149
NotSig 12044 11098 13140 13729
Up 651 2634 638 1426treat <- treat(fit.cont,lfc=0.5)
dt <- decideTests(treat)
summary(dt) c0 c1 c2 c3
Down 110 105 63 0
NotSig 15155 15083 15186 15092
Up 39 116 55 212par(mfrow=c(2,2))
for(i in 1:ncol(mycont)){
plotMD(treat,coef=i,status = dt[,i],hl.cex=0.5)
abline(h=0,col=colours()[c(226)])
lines(lowess(treat$Amean,treat$coefficients[,i]),lwd=1.5,col=4)
}
Write out marker genes for each cluster
contnames <- colnames(mycont)
for(i in 1:length(contnames)){
topsig <- topTreat(treat,coef=i,n=Inf)
write.csv(topsig,file=paste("./output/MarkerAnalysis/Epicardial/Development/DE/Cluster-",contnames[i],".csv",sep=""))
}fdr <- apply(treat$p.value, 2, function(x) p.adjust(x, method="BH"))
output <- data.frame(treat$genes,LogFC=treat$coefficients,AveExp=treat$Amean,tstat=treat$t, pvalue=treat$p.value, fdr=fdr)
write.csv(output,file="./output/MarkerAnalysis/Epicardial/Development/DE/MarkerAnalysis.csv")Perform gene set testing on C2 and GO sets
contnames <- colnames(mycont)
load("./output/RDataObjects/human_c2_v5p2.rdata")
load("./output/RDataObjects/human_c5_v5p2.rdata")
c2.id <- ids2indices(Hs.c2,treat$genes$ENTREZID)
c5.id <- ids2indices(Hs.c5,treat$genes$ENTREZID)
reactome.id <-c2.id[grep("REACTOME",names(c2.id))]
c2.c0 <- cameraPR(treat$t[,1],c2.id)
reactome.c0 <- cameraPR(treat$t[,1],reactome.id)
go.c0 <- cameraPR(treat$t[,1],c5.id)
for(i in 1:length(contnames)){
write.csv(cameraPR(treat$t[,i],c2.id),file=paste("./output/MarkerAnalysis/Epicardial/Development/GeneSetTests/c2-",contnames[i],".csv",sep=""))
write.csv(cameraPR(treat$t[,i],reactome.id),file=paste("./output/MarkerAnalysis/Epicardial/Development/GeneSetTests/reactome-",contnames[i],".csv",sep=""))
write.csv(cameraPR(treat$t[,i],c5.id),file=paste("./output/MarkerAnalysis/Epicardial/Development/GeneSetTests/go-",contnames[i],".csv",sep=""))
}Check quality of clusters
The quality of the clusters look good.
par(mfrow=c(1,1))
numgenes <- colSums(all.keep!=0)
boxplot(numgenes~grp)
Heatmap of marker genes
sam <- factor(epic.integrated$biorep,levels=c("f1","f2","f3","y1","y2","y3","a1","a2","a3"))
newgrp <- paste(grp,sam,sep=".")
newgrp <- factor(newgrp,levels=paste(rep(levels(grp),each=9),levels(sam),sep="."))
o <-order(newgrp)
clust <- rep(levels(grp),each=9)
samps <- rep(levels(sam),length(levels(grp)))Summarise expression across cells
sumexpr <- matrix(NA,nrow=nrow(logcounts),ncol=length(levels(newgrp)))
rownames(sumexpr) <- rownames(logcounts)
colnames(sumexpr) <- levels(newgrp)
for(i in 1:nrow(sumexpr)){
sumexpr[i,] <- tapply(logcounts[i,],newgrp,mean)
}sig.genes <- gene.label <- vector("list", length(levels(grp)))
for(i in 1:length(sig.genes)){
top <- topTreat(treat,coef=i,n=Inf)
sig.genes[[i]] <- rownames(top)[top$logFC>0][1:10]
gene.label[[i]] <- paste(rownames(top)[top$logFC>0][1:10],levels(grp)[i],sep="-")
}
csig <- unlist(sig.genes)
genes <- unlist(gene.label)
myColors <- list(Clust=NA,Sample=NA)
myColors$Clust<-sample(ggplotColors(length(levels(grp))),length(levels(grp)))
names(myColors$Clust)<-levels(grp)
myColors$Sample <- sample(ggplotColors(length(levels(sam))),length(levels(sam)))
names(myColors$Sample) <- levels(sam)
pdf(file="./output/Figures/NormalDev/epic-heatmap-siggenes-summarised-FYA-filtered.pdf",width=20,height=20,onefile = FALSE)
aheatmap(sumexpr[csig,],Rowv = NA,Colv = NA, labRow = genes,
annCol=list(Clust=clust,Sample=samps),
annColors=myColors,
fontsize=16,color="-RdYlBu",
scale="none")
dev.off()png
2 aheatmap(sumexpr[csig,],Rowv = NA,Colv = NA, labRow = genes,
annCol=list(Clust=clust,Sample=samps),
annColors=myColors,
fontsize=16,color="-RdYlBu",
scale="none")
Heatmap of pre-identified heart genes
hm <- read.delim("./data/heart-markers-long.txt",stringsAsFactors = FALSE)
hgene <- toupper(hm$Gene)
hgene <- unique(hgene)
m <- match(hgene,rownames(sumexpr))
m <- m[!is.na(m)]
mycelltypes <- hm$Celltype[match(rownames(sumexpr)[m],toupper(hm$Gene))]
mycelltypes <- factor(mycelltypes)
mygenes <- rownames(sumexpr)[m]
mygenelab <- paste(mygenes,mycelltypes,sep="_")
pdf(file="./output/Figures/NormalDev/epic-heatmap-hmarkers-summarised-FYA-filtered.pdf",width=20,height=15,onefile = FALSE)
aheatmap(sumexpr[m,],Rowv = NA,Colv = NA, labRow = mygenelab,
annCol=list(Clust=clust,Sample=samps),
# annRow=list(Celltypes=mycelltypes),
annColors=myColors,
fontsize=14,color="-RdYlBu")
dev.off()png
2 aheatmap(sumexpr[m,],Rowv = NA,Colv = NA, labRow = mygenelab,
annCol=list(Clust=clust,Sample=samps),
# annRow=list(Celltypes=mycelltypes),
annColors=myColors,
fontsize=14,color="-RdYlBu")
sessionInfo()R version 3.6.0 (2019-04-26)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: CentOS release 6.7 (Final)
Matrix products: default
BLAS: /usr/local/installed/R/3.6.0/lib64/R/lib/libRblas.so
LAPACK: /usr/local/installed/R/3.6.0/lib64/R/lib/libRlapack.so
locale:
[1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
[3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
[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
attached base packages:
[1] splines parallel stats4 stats graphics grDevices utils
[8] datasets methods base
other attached packages:
[1] dplyr_0.8.3 clustree_0.4.0
[3] ggraph_1.0.2 workflowr_1.3.0
[5] NMF_0.21.0 bigmemory_4.5.33
[7] cluster_2.1.0 rngtools_1.4
[9] pkgmaker_0.27 registry_0.5-1
[11] scran_1.12.0 SingleCellExperiment_1.6.0
[13] SummarizedExperiment_1.14.1 GenomicRanges_1.36.0
[15] GenomeInfoDb_1.20.0 DelayedArray_0.10.0
[17] BiocParallel_1.18.1 matrixStats_0.55.0
[19] cowplot_1.0.0 monocle_2.12.0
[21] DDRTree_0.1.5 irlba_2.3.3
[23] VGAM_1.1-1 ggplot2_3.2.1
[25] Matrix_1.2-17 Seurat_3.0.3.9019
[27] org.Hs.eg.db_3.8.2 AnnotationDbi_1.46.1
[29] IRanges_2.18.1 S4Vectors_0.22.0
[31] Biobase_2.44.0 BiocGenerics_0.30.0
[33] RColorBrewer_1.1-2 edgeR_3.26.3
[35] limma_3.40.2
loaded via a namespace (and not attached):
[1] reticulate_1.13 R.utils_2.9.0
[3] tidyselect_0.2.5 RSQLite_2.1.2
[5] htmlwidgets_1.5 grid_3.6.0
[7] combinat_0.0-8 docopt_0.6.1
[9] Rtsne_0.15 munsell_0.5.0
[11] codetools_0.2-16 ica_1.0-2
[13] statmod_1.4.30 future_1.14.0
[15] withr_2.1.2 colorspace_1.4-1
[17] fastICA_1.2-2 knitr_1.25
[19] ROCR_1.0-7 gbRd_0.4-11
[21] listenv_0.7.0 labeling_0.3
[23] Rdpack_0.11-0 git2r_0.26.1
[25] slam_0.1-45 GenomeInfoDbData_1.2.1
[27] polyclip_1.10-0 farver_1.1.0
[29] bit64_0.9-7 pheatmap_1.0.12
[31] rprojroot_1.3-2 vctrs_0.2.0
[33] xfun_0.10 R6_2.4.0
[35] doParallel_1.0.15 ggbeeswarm_0.6.0
[37] rsvd_1.0.2 locfit_1.5-9.1
[39] bitops_1.0-6 assertthat_0.2.1
[41] SDMTools_1.1-221.1 scales_1.0.0
[43] beeswarm_0.2.3 gtable_0.3.0
[45] npsurv_0.4-0 globals_0.12.4
[47] tidygraph_1.1.2 rlang_0.4.0
[49] zeallot_0.1.0 lazyeval_0.2.2
[51] checkmate_1.9.4 yaml_2.2.0
[53] reshape2_1.4.3 backports_1.1.5
[55] tools_3.6.0 gridBase_0.4-7
[57] gplots_3.0.1.1 dynamicTreeCut_1.63-1
[59] ggridges_0.5.1 Rcpp_1.0.2
[61] plyr_1.8.4 zlibbioc_1.30.0
[63] purrr_0.3.2 RCurl_1.95-4.12
[65] densityClust_0.3 pbapply_1.4-1
[67] viridis_0.5.1 zoo_1.8-6
[69] ggrepel_0.8.1 fs_1.3.1
[71] magrittr_1.5 data.table_1.12.4
[73] lmtest_0.9-37 RANN_2.6.1
[75] whisker_0.3-2 fitdistrplus_1.0-14
[77] lsei_1.2-0 evaluate_0.14
[79] xtable_1.8-4 sparsesvd_0.1-4
[81] gridExtra_2.3 HSMMSingleCell_1.4.0
[83] compiler_3.6.0 scater_1.12.2
[85] tibble_2.1.3 KernSmooth_2.23-15
[87] crayon_1.3.4 R.oo_1.22.0
[89] htmltools_0.4.0 tidyr_0.8.3
[91] DBI_1.0.0 tweenr_1.0.1
[93] MASS_7.3-51.4 R.methodsS3_1.7.1
[95] gdata_2.18.0 metap_1.1
[97] igraph_1.2.4.1 pkgconfig_2.0.3
[99] bigmemory.sri_0.1.3 plotly_4.9.0
[101] foreach_1.4.7 vipor_0.4.5
[103] dqrng_0.2.1 XVector_0.24.0
[105] bibtex_0.4.2 stringr_1.4.0
[107] digest_0.6.21 sctransform_0.2.0
[109] RcppAnnoy_0.0.12 tsne_0.1-3
[111] rmarkdown_1.14 DelayedMatrixStats_1.6.0
[113] gtools_3.8.1 nlme_3.1-141
[115] jsonlite_1.6 BiocNeighbors_1.2.0
[117] viridisLite_0.3.0 pillar_1.4.2
[119] lattice_0.20-38 httr_1.4.1
[121] survival_2.44-1.1 glue_1.3.1
[123] qlcMatrix_0.9.7 FNN_1.1.3
[125] png_0.1-7 iterators_1.0.12
[127] bit_1.1-14 ggforce_0.3.0
[129] stringi_1.4.3 blob_1.2.0
[131] BiocSingular_1.0.0 caTools_1.17.1.2
[133] memoise_1.1.0 future.apply_1.3.0
[135] ape_5.3