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GSE75748_temp.R
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library(ggplot2)
library(gridExtra)
library(mixtools)
library(dplyr)
library(gplots)
library(RColorBrewer)
library(diptest)
library(doParallel)
library(Rtsne)
########### import and process data
mydir <- "/mnt/gtklab01/ahjung/bivalent-tmp/GSE75748/"
## bulk_cell <- read.csv(paste0(mydir,"GSE75748_bulk_cell_type_ec.csv"),row.names=1)
## bulk_time <- read.csv(paste0(mydir,"GSE75748_bulk_time_course_ec.csv"),row.names=1)
sc_cell <- read.csv(paste0(mydir,"GSE75748_sc_cell_type_ec.csv"),row.names=1)
sc_time <- read.csv(paste0(mydir,"GSE75748_sc_time_course_ec.csv"),row.names=1)
extract_coldata_sub <- function(df,i){
df_split <- strsplit(colnames(df)[i],"_|[.]")[[1]]
coldata <- data.frame("cell"=df_split[1],
"exp"=df_split[2],
"idx"=df_split[3])
return(coldata)}
extract_coldata <- function(df) {
coldata <- extract_coldata_sub(df,1)
invisible(sapply(2:ncol(df),
function(i) coldata <<- rbind(coldata,
extract_coldata_sub(df,i))))
return(coldata)}
sc_cell_coldata <- extract_coldata(sc_cell)
sc_time_coldata <- extract_coldata(sc_time)
## bulk_time_coldata <- extract_coldata(bulk_time)
## bulk_cell_coldata <- data.frame("cell"=strsplit(colnames(bulk_cell)[1],"_")[[1]][1],
## "rep"=strsplit(colnames(bulk_cell)[1],"_")[[1]][2])
## invisible(sapply(2:ncol(bulk_cell),
## function(i) bulk_cell_coldata <<- rbind(bulk_cell_coldata,
## data.frame("cell"=strsplit(colnames(bulk_cell)[i],"_")[[1]][1],
## "rep"=strsplit(colnames(bulk_cell)[i],"_")[[1]][2]))))
#hist(log(as.numeric(sc_cell[1,sc_cell_coldata$cell == "H1"])+1))
get_scexp <- function(sc_cell,sc_cell_coldata,celltype,mark,markid) {
h1id <- unique(t2g$ext_gene[t2g$target_id %in% get(mark)])
# h1id <- unique(t2g$ext_gene[t2g$target_id %in% a])
h1id <- h1id[!h1id == ""]
x <- log(as.numeric(unlist(
sc_cell[rownames(sc_cell) %in% h1id,sc_cell_coldata$cell %in% celltype]))+1)
cat("equal/below 2 is", length(x[x<=2]), "above 2 is", length(x[x>2]),"\n")
x2 <- data.frame("exp"=x[x>2], ######### filter any counts below 2
"class"=rep(markid,length(x[x>2])))
return(x2) }
get_bulkexp <- function(celltype,mark,markid) {
h1id <- unique(t2g$ext_gene[t2g$target_id %in% get(mark)])
# h1id <- unique(t2g$ext_gene[t2g$target_id %in% a])
h1id <- h1id[!h1id == ""]
x <- apply(bulk_cell[rownames(bulk_cell) %in% h1id,
bulk_cell_coldata$cell %in% celltype],1,mean)
x <- log(as.numeric(unlist(x))+1)
x2 <- data.frame("exp"=x[x>2], "class"=rep(markid,length(x[x>2])))
return(x2)}
#=====================================
## sc_test_tb <- ggplot(allexp, aes(x=exp, col=class)) + geom_density(size=2)+
## ggtitle("sc: TB ; chip: BMP4T")
## grid.arrange(h1, bmp4m_dec, bmp4t, test,ncol=1)
## grid.arrange(bmp4t, test, h1,ncol=1)
## grid.arrange(bulk_test, bulk_test_tb, sc_test, sc_test_tb,ncol=2)
## grid.arrange(bulk_test, sc_test, ncol=1)
## plot_hist("EC","BMP4M_1_bivalent"),ylim=c(0,0.3),col="black")
## lines(plot_hist("EC","BMP4M_1_H3K4me3"),col="blue")
## lines(plot_hist("EC","BMP4M_1_H3K27me3"),col="red")
## #plot_hist(c("DEC","EC"),"H1hesc_bivalent")
## get_scexp2 <- function(celltype,mark, markid) {
## h1id <- unique(t2g$ext_gene[t2g$target_id %in% mark])
## h1id <- h1id[!h1id == ""]
## x <- log(as.numeric(unlist(
## sc_cell[rownames(sc_cell) %in% h1id,sc_cell_coldata$cell %in% celltype]))+1)
## x2 <- data.frame("exp"=x[x>2], "class"=rep(markid,length(x[x>2])))
## return(x2)}
## a <- hESC_1_bivalent[hESC_1_H3K27me3 %in% BMP4M_1_bivalent]
## b <- hESC_1_bivalent[hESC_1_H3K27me3 %in% BMP4M_1_H3K4me3]
## c <- hESC_1_bivalent[hESC_1_H3K27me3 %in% BMP4M_1_H3K27me3]
## allexp <- rbind(get_scexp2("DEC", a,"bivalent"),
## get_scexp2("DEC", b,"H3K4me3"),
## get_scexp2("DEC", c,"H3K27me3"))
## test4a <- ggplot(allexp, aes(x=exp, col=class)) + geom_density(size=2)+
## ggtitle("hESC H3K27me3 genes in BMP4M")
## grid.arrange(test2,test3,test4,
## test2a,test3a,test4a,ncol=3)
## df <- data.frame("x"=bulk_cell[bulk_cell[,5] > 0,5])
## ggplot(log(df),aes(x=x)) + geom_density()
## df <- data.frame("x"=sc_cell[sc_cell[,3] > 0,3])
## grid.arrange(ggplot(log(df),aes(x=x)) + geom_density(),
## h1,ncol=1)
############# mixed gaussian model
## tx2gene <- function(){
## mart <- biomaRt::useMart(biomart = "ENSEMBL_MART_ENSEMBL",
## dataset = "hsapiens_gene_ensembl",
## host="grch37.ensembl.org",
## path="/biomart/martservice")
## t2g <- biomaRt::getBM(attributes = c("ensembl_transcript_id",
## "ensembl_gene_id",
## "hgnc_symbol",
## "refseq_mrna",
## "chromosome_name",
## "start_position",
## "end_position",
## "strand"), mart = mart)
## t2g <- dplyr::rename(t2g, target_id = ensembl_transcript_id,
## ens_gene = ensembl_gene_id, ext_gene = hgnc_symbol,
## refseq = refseq_mrna,
## chr = chromosome_name)
## return(t2g)
## }
## t2g <- tx2gene()
## t2g <- cbind(t2g,t2g$end_position-t2g$start_position)
## t2g$chromosome <- as.integer(t2g$chr)
## t2g <- t2g[!is.na(t2g$chromosome),]
## t2g$chromosome2 <- paste("chr",t2g$chr,sep="")
## colnames(t2g) <- c("target_id","ens_gene","ext_gene","refseq","chrn","start","end","strand","length","chrn2","chr")
## t2g <- t2g[,c("target_id","ens_gene","ext_gene","refseq","chr","length")]
## save(t2g,file="/mnt/gtklab01/ahjung/bivalent/t2g_modalv.RData")
load("/mnt/gtklab01/ahjung/bivalent/t2g_modalv.RData")
#================================
plot_mix_comps <- function(x, mu, sigma, lam) {
lam * dnorm(x, mu, sigma)
}
plot_mixmdl <- function(mixmdl, mark) {
p <- data.frame(x = mixmdl$x) %>%
ggplot() +
geom_histogram(aes(x, ..density..), binwidth = 0.1, colour = "black",
fill = "white") +
stat_function(geom = "line", fun = plot_mix_comps,
args = list(mixmdl$mu[1], mixmdl$sigma[1], lam = mixmdl$lambda[1]),
colour = "red", lwd = 1.5) +
stat_function(geom = "line", fun = plot_mix_comps,
args = list(mixmdl$mu[2], mixmdl$sigma[2], lam = mixmdl$lambda[2]),
colour = "blue", lwd = 1.5) + xlim(0,12.5) +
ylab("Density") +
ggtitle(paste0(mark,"; mu is ",
round(mixmdl$mu[1],digits=2),
", ",
round(mixmdl$mu[2],digits=2),
"; lambda is ",
round(mixmdl$lambda[1],digits=2),
", ",
round(mixmdl$lambda[2],digits=2))) +
theme_bw()
return(p)
}
get_mixmdl <- function(allexp, mark) {
set.seed(1)
wait <- allexp$exp[allexp$class %in% mark]
mixmdl <- normalmixEM(wait, k = 2)
return(mixmdl)
}
plot_mg <- function(allexp, mark) {
mixmdl <- get_mixmdl(allexp, mark)
p <- plot_mixmdl(mixmdl, mark)
return(p)
}
raincloud_theme <- theme(
text = element_text(size = 10),
axis.title.x = element_text(size = 16),
axis.title.y = element_text(size = 16),
axis.text = element_text(size = 14),
# axis.text.x = element_text(angle = 45, vjust = 0.5),
legend.title=element_text(size=16),
legend.text=element_text(size=16),
legend.position = "right",
plot.title = element_text(lineheight=.8, face="bold", size = 16),
panel.border = element_blank(),
panel.grid.minor = element_blank(),
panel.grid.major = element_blank(),
axis.line.x = element_line(colour = 'black', size=0.5, linetype='solid'),
axis.line.y = element_line(colour = 'black', size=0.5, linetype='solid'))
plot_cluster <- function(nclass,expvalue,gene,cols,xlim=c(-2,8),ylim=c(0,0.6)){
length(nclass)
cumsum(nclass)
#cols <- brewer.pal(length(nclass),"Set1")
plot(density(expvalue[1:cumsum(nclass)[1]]),
col=cols[1],
xlim=xlim,
ylim=ylim,
main=gene)
for (i in 2:(length(nclass)-1)) {
lines(density(expvalue[cumsum(nclass)[i]:(cumsum(nclass)[i+1]-1)]),
col=cols[i])
}
#color <- ifelse(logivalue, "red", "blue")
## plot(density(expvalue[1:212]),col="black",xlim=c(-2,10),ylim=c(0,1),main=gene)
## lines(density(expvalue[213:350]),col="red")
## lines(density(expvalue[351:455]),col="blue")
## lines(density(expvalue[456:524]),col="green")
}
get_colors <- function(n) {
return(brewer.pal(length(n),"Set2"))
}
color_check <- function(cols, coldata) {
barplot(rep(1,length(nclass)),
col=cols,
names.arg=unique(coldata),
las=2)
}
return_density <- function(expvalue_sub,name){
exptest <- density(expvalue_sub)
return(data.frame("x"=exptest$x,"y"=exptest$y,"name"=name))
}
plot_time <- function(expvalue,gene,coldatatbl) {
ncate <- c(0,as.numeric(cumsum(table(coldatatbl))))
tblnames <- names(table(coldatatbl))
densitytbl <- return_density(expvalue[(ncate[1]+1):ncate[1+1]],tblnames[1])
sapply(2:(length(ncate)-1), function(x)
densitytbl <<- rbind(densitytbl,return_density(expvalue[(ncate[x]+1):ncate[x+1]],tblnames[x])))
p <- ggplot(data=densitytbl, aes(x=x,y=y, col=name)) + geom_line() + facet_wrap(~name,ncol=1)
return(p)
## plot(density(expvalue[1:ncate[2]]),
## col="black",xlim=c(-2,10),ylim=c(0,1),main=gene)
## invisible( sapply(2:(length(ncate)-1), function(x)
## lines(density(expvalue[(ncate[x]+1):ncate[x+1]]),col=cols[x])))
}
exp_cluster <- function(expvalue,gene){
#color <- ifelse(logivalue, "red", "blue")
return(data.frame("gene"=gene,
"H1"=median(expvalue[1:212]),
"DEC"=median(expvalue[213:350]),
"EC"=median(expvalue[351:455]),
"TB"=median(expvalue[456:524])))
}
get_tpm <- function(df) {
## get gene lengths
x <- t2g[t2g$ext_gene %in% rownames(df),]
x <- unique(x[,c("ext_gene","length")])
x <- x[!duplicated(x$ext_gene),]
df2 <- df[unique(x$ext_gene),]
xlength <- x$length/1000
df2_rpk <- as.matrix(df2)/xlength
rownames(df2_rpk) <- rownames(df2)
df2_tpmfactor <- apply(df2_rpk,2, sum)
df2_tpm <- t(t(df2_rpk) / df2_tpmfactor)
df2_tpm <- df2_tpm[!apply(df2_tpm,1,sum)==0,]
df2_tpm <- df2_tpm*1000000
return(df2_tpm)
}
test_bimodal <- function(exp) {
a <- dip.test(as.numeric(exp))
return(a$p.value)
}
## exp <- sc_cell
## a <- exp[,1:212]
## c <- apply(a,1,test_bimodel)
## cellfactors <- as.numeric(sc_cell_coldata$cell[sc_cell_coldata$cell %in% c("H1","DEC","EC","TB")])
## cellfactors[cellfactors==3] <- 2
## cellfactors[cellfactors==4] <- 3
## cellfactors[cellfactors==7] <- 4
fit_bimodal <- function(exp,name) {
tryCatch(mixmdl <- normalmixEM(exp, k=2, mu=c(0,6),maxit = 100),error=function(e){NA})
if (!exists("mixmdl")) {
df <- data.frame("gene"=NA,
"mu1"=NA,
"mu2"=NA,
"lambda1"=NA,
"lambda2"=NA,
"converge"=NA)
} else {
df <- data.frame("gene"=name,
"mu1"=mixmdl$mu[1],
"mu2"=mixmdl$mu[2],
"lambda1"=mixmdl$lambda[1],
"lambda2"=mixmdl$lambda[2],
"converge"=rev(mixmdl$all.loglik)[1] == rev(mixmdl$all.loglik)[2] )
}
return(df)
}
messup <- function(sc_cell) {
# sc_cell must be in log scale
nnoise <- matrix(rnorm(nrow(sc_cell)*ncol(sc_cell),
mean=0, sd=0.1),ncol=ncol(sc_cell))
sc_cell_messup <- sc_cell+nnoise
return(sc_cell_messup)
}
fit_bimodal_multi <- function(logexp_messup) {
cl <- makeCluster(detectCores()-2) # create a cluster with max-2 cores
registerDoParallel(cl) # register the cluster
res_tpm = foreach(i = 1:nrow(logexp_messup),
.combine = "rbind",
.packages = "mixtools",
.export = c("fit_bimodal")) %dopar% {
# generate a bootstrap sample
fit_bimodal(logexp_messup[i,],rownames(logexp_messup)[i])
}
stopCluster(cl) # shut down the cluster
return(res_tpm)
}
filter_condition <- function(res_diptest, res_tpm, cond_dip=0.01, cond_lambda=0.7, cond_mu=1) {
## Default conditions are 0.01,0.7,1
condition <- (res_diptest <= cond_dip) &
sapply(1:nrow(res_tpm),
function(i) min(res_tpm[i,]$mu1, res_tpm[i,]$mu2) <= cond_mu) &
sapply(1:nrow(res_tpm),
function(i) max(res_tpm[i,]$mu1, res_tpm[i,]$mu2) > cond_mu) &
sapply(1:nrow(res_tpm),
function(i) res_tpm[i,]$lambda1 <= cond_lambda)
return(condition)
}
findtrough <- function(densityy){
trough <- which(diff(sign(diff(densityy)))==2)
trough <- ifelse(length(trough)>1,trough[1],trough)
return(trough)
}
findcutoff <- function(exp) {
dens <- density(exp)
return(dens$x[findtrough(dens$y)])
}
binarizeexp <- function(exp) {
cutoff <- findcutoff(exp)
return(exp > cutoff)
}
plottrough <- function(exp){
dens <- density(exp)
trough <- findtrough(dens$y)
plot(dens,main=rownames(exp))
abline(v=dens$x[trough],col="red")
}