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00_Alz_DNAm_methylation_functions.R
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00_Alz_DNAm_methylation_functions.R
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## cset: result of cpgCollapse()$cobj
## block450: results of blockFinder
## coi = covariate of interest
## N: the number of blocks to plot, default=10
## blockname = name of block track, default='coi'
## filename = where to save plots
## scale, in kb. default = 100
blockPlot = function(cset, blocks450, coi, N=10,
blockname = "coi", filename=paste0(blockname,"_blocks.pdf"),scale=100,
showMethPanel = TRUE, showGenePanel=TRUE, showDiffPanel=TRUE,
showCancerPanel = TRUE, bty= "o") {
panels = c(showMethPanel,showGenePanel, showDiffPanel, showCancerPanel)
require(GenomicRanges)
blocksTable=with(blocks450$table, GRanges(seqnames=chr,ranges=IRanges(start=start,end=end)))
colIds=match(c("chr","start","end"),names(blocks450$table))
mcols(blocksTable)=blocks450$table[-colIds]
plotRegion = blocksTable[1:N]
## annotation based on ensembl
cat("Loading Annotation.\n")
load("/users/ajaffe/GenomicStates/GenomicState.Hsapiens.ensembl.GRCh37.p12.rda")
gs = GenomicState.Hsapiens.ensembl.GRCh37.p12$fullGenome
oo = findOverlaps(blocksTable, gs)
anno = split(gs[subjectHits(oo)], queryHits(oo))
# cancer blocks
if(showCancerPanel) {
load("/users/ajaffe/Lieber/Projects/450k/devPaper/cancer_blocks_hansen.rda")
genome(blocks)="hg19"
cancerBlocks = blocks
}
cat("Ploting.")
pdf(filename,height=5,width=10)
par(bty=bty)
for(i in seq(along=plotRegion)) {
cat(".")
r = plotRegion[i]
tmp=subsetByOverlaps(cset,r)
tmp450=sort(subsetByOverlaps(blocksTable,r))
if(showCancerPanel) tmpBsmooth=subsetByOverlaps(cancerBlocks,r)
beta=getBeta(tmp)
x=start(tmp)
ii=cset %over% r
d=blocks450$coef[ii]
sd=blocks450$fitted[ii]
## which rows
Index=split(seq_along(coi),coi)
mns=sapply(Index,function(ind) rowMeans(beta[,ind]))
smns=apply(mns,2,function(y) limma::loessFit(y,x,span=.2)$fitted)
## paneling, from hector
mypar(1,1, brewer.name = "Set1")
par(mar=par()$mar+c(0,3,0,0))
omar=par()$mar
cmar=omar
cmar[1]=.5
par(mar=cmar)
layout(cbind(1:sum(panels)),height=c(1+2/3,1, 0.75,0.75)[1:sum(panels)])
if(showMethPanel) {
matplot(x,beta,col=as.numeric(factor(coi)),type="p",pch=".",
xlab="",ylab="",xaxt="n",yaxt="n",ylim=c(0,1))
matplot(x,smns,col=1:2,type="l",lwd=2.5,add=TRUE,lty=1)
legend("bottomright",col=seq(along=levels(factor(coi))),
lty=1,lwd=2,legend=levels(factor(coi)),cex=.8, bty=bty)
segments(min(x), .2, min(x)+scale*1000, .2)
text(min(x),.05,labels=sprintf("%dkb",scale),pos=4,offset=0)
axis(side=2,at=c(.2,.5,.8), cex.axis=1.8)
mtext(sprintf("%s:%d-%d", seqnames(r), start(r), end(r)), side=3)
mtext("Methylation",side=2, line = 2.5,cex=1.5)
legend("topright", paste0("fwer = ",
signif(blocks450$tab$fwer[i],3)),bty=bty)
}
# annotation
if(showGenePanel) {
cmar=omar
if(!is.na(showDiffPanel)) {
cmar[3]=0.5
} else {
cmar[c(1,3)]=c(0.5,0.5)
}
par(mar=cmar)
plot(x,rep(0,length(x)), type="n",ylim=c(-1.5,1.5),yaxt="n",ylab="",
xlab="",cex.axis = 1.5, cex.lab =1.5,xaxt="n")
a = as.data.frame(anno[[i]])
Strand = ifelse(a$strand == "+", 1, ifelse(a$strand=="-", -1, 0))
Col = ifelse(a$theRegion == "exon", "blue", ifelse(a$theRegion == "intron", "lightblue","white"))
Lwd = ifelse(a$theRegion == "exon", 1, ifelse(a$theRegion == "intron",0.5,0))
axis(2,c(-1,1),c("-","+"),tick=FALSE,las=1,cex.axis = 3)
abline(h=0,lty=3)
for(k in 1:nrow(a)) {
polygon(c(a$start[k],a$end[k],a$end[k],a$start[k]),
Strand[k]/2+c(-0.3,-0.3,0.3,0.3)*Lwd[k],col=Col[k])
}
## by gene
g = split(a, sapply(a$symbol,"[", 1))
# g = split(a, a$Symbol)
s2 = ifelse(sapply(g, function(x) unique(x$strand))=="+",1,-1)
g = sapply(g, function(x) (max(x$end) - min(x$start))/2 + min(x$start) )
if(length(g) > 0) text(g, y=s2, names(g),font=1,pos=s2+2,cex=0.8)
mtext("Genes",side=2, line = 2.5,cex=1.5)
if(!showDiffPanel) {
xtick=pretty(x)
axis(side=1,at=xtick,labels=sprintf("%.1fMb",xtick / 1e6))
}
}
## mean diff
if(showDiffPanel) {
cmar=omar
cmar[c(1,3)]=c(.5,.5)
par(mar=cmar)
zz=granges(tmp)
matplot(x,sd,xaxt="n",ylab="",xlab="",type="n",lty=1,ylim=c(-.6,.6),yaxt="n",pch=21)
axis(side=2,at=c(-.3,0,.3),labels=c("-.3","0",".3"))
xtick=pretty(x)
axis(side=1,at=xtick,labels=sprintf("%.1fMb",xtick / 1e6))
mtext("Diff",side=2, line = 2.5,cex=1.5)
ii=which(zz$type=="OpenSea")
blockgroup=zz$blockgroup[ii]
blockIndexes=split(seq(along=blockgroup),blockgroup)
for (ind in blockIndexes) {
ind=ii[ind]
lines(x[ind], sd[ind], lwd=2.5,col="black")
}
points(x[ii],d[ii],pch=21,cex=1.4,bg="black")
axis(side=2,at=c(-2,0,2))
abline(h=0,lty=2,col="black")
cmar=omar
cmar[3]=.5
par(mar=cmar)
matplot(x,beta,type="n",xaxt="n",yaxt="n",xlab="",
ylab="",ylim=c(0,2),bty="n")
}
# browser()
if(showCancerPanel) {
col=ifelse(tmp450$value<0 & tmp450$p.value<.05,"blue",ifelse(tmp450$value>0 & tmp450$p.value<.05,"red","black"))
rect(start(tmp450),1+1/3,end(tmp450),1+2/3,col=col)
if(length(tmpBsmooth) > 0) rect(start(tmpBsmooth),1/3,end(tmpBsmooth),2/3,col=ifelse(tmpBsmooth$direction=="hypo","blue","red"))
axis(side=2,at=c(.5,1.5),labels=c("Hansen et al.",blockname),las=1,lwd=0)
legend("bottomleft",pt.bg=c("blue","red"),legend=c("hypo","hyper"),pch=22,cex=.8)
}
}
dev.off()
}
parsePheno = function(pd) {
require(Hmisc)
phenoIndex=grep("characteristics",colnames(pd))
# column names
colNames = apply(pd[,phenoIndex], 2, function(x) {
ss(as.character(x),": ", 1)[1]
})
colNames = capitalize(colNames) # in Hmisc
colnames(pd)[phenoIndex] = colNames
#variables themselves
pd[,phenoIndex] = apply(pd[,phenoIndex], 2, function(x) {
ss(as.character(x),": ", 2)
})
pd$title = as.character(pd$title)
pd$geo_accession = as.character(pd$geo_accession)
tmp = colSums(apply(pd, 2, as.numeric))
for(i in which(!is.na(tmp))) pd[,i] = as.numeric(pd[,i])
rownames(pd) = pd$geo_accession
return(pd)
}
require(RColorBrewer)
mypar = function(a=1,b=1,brewer.n=8,brewer.name="Dark2",...){
par(mar=c(2.5,2.5,1.6,1.1),mgp=c(1.5,.5,0))
par(mfrow=c(a,b),...)
palette(brewer.pal(brewer.n,brewer.name))
}
makingCOI = function(coi, n , returnMean = TRUE) {
groups = cut(coi, quantile(coi,
prob=seq(0,1,1/(n-1)), na.rm=T),include.lowest=T,
labels = F)
if(returnMean) {
gIndexes = splitit(groups)
groupLabels = rep(0,length(groups))
for(m in seq(along=gIndexes)) {
groupLabels[gIndexes[[m]]] = signif(mean(coi[gIndexes[[m]]]),2)
}
groupLabels = factor(groupLabels)
return(groupLabels)
} else return(groups)
}
## from Rafa
dogo <- function(names,universe,species="human", goP = 0.01,
cond=FALSE, ontology = "BP"){
if(species=="human"){
golib="org.Hs.eg.db"
library(golib,character.only=TRUE)
gomap= org.Hs.egREFSEQ2EG
} else if (species == "mouse") {
golib="org.Mm.eg.db"
library(golib,character.only=TRUE)
gomap= org.Mm.egREFSEQ2EG
} else if (species == "rat") {
golib="org.Rn.eg.db"
library(golib,character.only=TRUE)
gomap= org.Rn.egREFSEQ2EG
}
require(GOstats)
x=unlist(mget(as.character(names), gomap,ifnotfound = NA))
x=x[!is.na(x)]
Universe=unlist(mget(as.character(universe),gomap,ifnotfound = NA))
Universe=unique(c(Universe[!is.na(Universe)],unique(x)))
params <- new("GOHyperGParams", geneIds = unique(x),
universeGeneIds = Universe,
annotation = golib,
ontology = ontology, pvalueCutoff = goP, conditional = cond,
testDirection="over")
ht=hyperGTest(params)
tab=summary(ht)
tmp1=geneIdsByCategory(ht)
tmp1=tmp1[tab[,1]]
tab$IDs=sapply(tmp1,function(y) paste(names(x)[x%in%y],collapse=";"))
return(tab)
}
#### to-do: replace with derfinderPlot:plotCoverage
dmrPlot = function(regions, p, chr, pos, cluster, genes, coi,
genomicState, build="hg19", species = "human", Jitter = FALSE,
number=100,cols=NULL, lines = FALSE, linesSmooth = TRUE,
title = TRUE, Legend = TRUE, colorRamp = FALSE,
meanSmooth=TRUE, plotCpG = TRUE, geneAnno = "gene") {
require(bumphunter)
require(derfinder)
gr = GRanges(regions$chr, IRanges(regions$start, regions$end))
if(build == "hg18") {
cpg.cur = read.table("http://www.haowulab.org/software/makeCGI/model-based-cpg-islands-hg18.txt",
header = TRUE, as.is=TRUE)
library("BSgenome.Hsapiens.UCSC.hg18")
}
if(build == "hg19") {
cpg.cur = read.table("http://www.haowulab.org/software/makeCGI/model-based-cpg-islands-hg19.txt", header = TRUE, as.is=TRUE)
library("BSgenome.Hsapiens.UCSC.hg19")
}
if(build == "mm9") {
cpg.cur = read.table("http://www.haowulab.org/software/makeCGI/model-based-cpg-islands-mm9.txt",
header = TRUE, as.is=TRUE)
library("BSgenome.Mmusculus.UCSC.mm9")
}
if(build == "hg38") {
cpg.cur = read.table("/dcl01/lieber/ajaffe/Steve/meth450k_annotation_hg38/makeCGI/CGI-Hsapiens.txt",
header = TRUE, as.is=TRUE)
library("BSgenome.Hsapiens.UCSC.hg38")
}
ocpgi=data.frame(chr=I(cpg.cur[,1]),
start=as.numeric(cpg.cur[,2]),
end=as.numeric(cpg.cur[,3]))
ADD1 = 1500; PAD = 10
gr2 = GRanges(regions$chr, IRanges(regions$start - ADD1, regions$end + ADD1))
anno = annotateRegions(gr2, genomicState)$annotationList
# check regions
if(is.numeric(coi)) {
groups=coi
gNames= sort(unique(coi))
}
if(is.character(coi) | is.factor(coi)) {
groups = factor(coi)
gNames= levels(groups)
}
gIndexes=split(1:length(groups),groups)
brewer.n=max(3,min(length(unique(coi)),11))
if(is.null(cols)) {
mypar(brewer.n=brewer.n)
} else if(length(cols) == 1 & cols[1] %in% rownames(brewer.pal.info)) {
mypar(brewer.name=cols, brewer.n=brewer.n)
} else {
mypar()
palette(cols)
}
if(colorRamp) {
pal = colorRampPalette(palette()[3:brewer.n])
palette(pal(brewer.n))
}
cat("Plotting.\n")
for(j in 1:(min(nrow(regions), number))) {
layout(matrix(1:2,ncol=1),heights=c(0.7,0.3))
# first plot, region
par(mar=c(0,4.5,0.25,1.1),oma=c(0,0,2,0))
Index=(regions[j,7]-PAD):(regions[j,8]+PAD)
Index = Index[Index %in% seq(along=cluster)]
Index=Index[cluster[Index]==regions[j,6]]
# make DMR plots
if(Jitter) {
posx = matrix(pos[Index], nc = ncol(p), nr = length(Index),
byrow= FALSE)
posx = t(apply(posx, 1, function(x) jitter(x,amount = 12)))
} else posx = pos[Index]
matplot(posx, p[Index,], ylim = c(0,1),
ylab = "", xlab = "",xaxt = "n",cex=0.7,
cex.axis = 1.7, cex.lab = 1.7, pch=21,
bg = as.numeric(factor(groups)),col="black",
xlim = range(pos[Index]), yaxt="n")
axis(2, at = c(0.2, 0.5, 0.8), cex.axis=1.7)
xx=pos[Index]
for(k in seq(along=gIndexes)){
if(length(gIndexes[[k]]) == 1) yy=p[Index,gIndexes[[k]]]
if(length(gIndexes[[k]]) > 1) yy=rowMeans(p[Index,gIndexes[[k]]])
if(meanSmooth) {
fit1=loess(yy~xx,degree=1,span=300/(36*length(xx)),
family="symmetric")
lines(xx,fit1$fitted,col=k,lwd=2)
} else lines(xx,yy,col=k,lwd=2)
}
mtext("Methylation",side=2, line = 2.5,cex=1.8)
if(Legend) {
if(length(unique(coi)) < 4) {
legend("topleft",legend=gNames,col=1:length(gNames),
lty=1, lwd = 4,cex=1,bty="n")
} else {
legend("topleft",legend=gNames,col=1:length(gNames),
pch=19, pt.cex = 2,cex=1.1, nc = 6,bty="n")
}
}
abline(v=(regions$start[j]-15),lty=1)
abline(v=(regions$end[j]+15),lty=1)
if(title) mtext(paste0(genes$name[j],"; ",
genes$distance[j],"bp from tss:",genes$description[j]), outer=T,cex=1.3)
# add cpgs
if(plotCpG) {
thechr=as.character(regions$chr[j])
chrName = strsplit(thechr, "r")[[1]][2]
chrName = paste("Chromosome",chrName)
start = pos[Index[1]]
end = pos[Index[length(Index)]]
ocpgi2=ocpgi[ocpgi$chr%in%unique(as.character(thechr)),]
##PLOT CPG ISLANDS
if(species=="human") seq<-Hsapiens[[as.character(thechr) ]]
if(species=="mouse") seq<-Mmusculus[[as.character(thechr) ]]
subseq<-subseq(seq,start=start,end=end)
cpgs=start(matchPattern("CG",subseq))+start-1
if(plotCpG) Rug(cpgs,col="black")
Index1 = which(ocpgi2[,1]==as.character(thechr) &
((ocpgi2[,2] > start & ocpgi2[,2]< end) |
(ocpgi2[,3] > start & ocpgi2[,3]< end)))
if(length(Index1)>0) sapply(Index1,function(j) Rug(unlist(ocpgi2[j,2:3]),
col="darkgreen",lwd=3,side=1))
}
# plot 3
##PLOT GENES
par(mar=c(3.5,4.5,0.25,1.1))
plot(0,0, type="n", xlim=range(xx),ylim=c(-1.5,1.5),yaxt="n",ylab="",
xlab="",cex.axis = 1.5, cex.lab =1.5)
a = as.data.frame(anno[[j]])
Strand = ifelse(a$strand == "+", 1, ifelse(a$strand=="-", -1, 0))
Col = ifelse(a$theRegion == "exon", "blue", ifelse(a$theRegion == "intron", "lightblue","white"))
Lwd = ifelse(a$theRegion == "exon", 1, ifelse(a$theRegion == "intron",0.5,0))
axis(2,c(-1,1),c("-","+"),tick=FALSE,las=1,cex.axis = 3)
abline(h=0,lty=3)
for(k in 1:nrow(a)) {
polygon(c(a$start[k],a$end[k],a$end[k],a$start[k]),
Strand[k]/2+c(-0.3,-0.3,0.3,0.3)*Lwd[k],col=Col[k])
}
if(sum(a$theRegion=="exon") > 0) {
e = a[a$theRegion=="exon",]
s2 = Strand[a$theRegion=="exon"]
g = unlist(e$symbol)
g[is.na(g)] = ""
if(length(g) > 0) text(x=e$start + e$width/2,
y=s2*0.75, g,font=1,pos=s2+2,cex=1.2)
}
mtext("Genes",side=2, line = 2.5,cex=1.5)
mtext(chrName,side=1, line = 2,cex=1.4)
abline(v=(pos[regions[j,7]]-15),lty=1)
abline(v=(pos[regions[j,8]]+15),lty=1)
}
}
Rug <- function (x, ticksize = 0.03, side = 1, lwd = 0.5, col = par("fg"),
quiet = getOption("warn") < 0, ...) {
x <- as.vector(x)
ok <- is.finite(x)
x <- x[ok]
if (!quiet) {
u <- par("usr")
u <- if (side%%2 == 1) {
if (par("xlog"))
10^u[1:2]
else u[1:2]
}
else {
if (par("ylog"))
10^u[3:4]
else u[3:4]
}
if (any(x < u[1] | x > u[2]))
warning("some values will be clipped")
}
Axis(side = side, at = x, labels = FALSE, lwd = lwd, col = col,
tck = ticksize, ...)
}