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fPlots_ggplot2.R
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fPlots_ggplot2.R
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require(bit64)
require(Cairo)
require(ggplot2)
require(grid)
require(gridExtra)
require(plyr)
### Get path to repo
getSDIR <- function(){
args=commandArgs(trailing=F)
TAG="--file="
path_idx=grep(TAG,args)
SDIR=dirname(substr(args[path_idx],nchar(TAG)+1,nchar(args[path_idx])))
if(length(SDIR)==0) {
return(getwd())
} else {
return(SDIR)
}
}
copy.number.log.ratio = function(out, fit, load.genome=FALSE, gene.pos=NULL, col.1="#0080FF", col.2="#4CC4FF", sample.num=NULL, lend='butt', theme='bw', subset.indices = NULL, plotX = FALSE){
mat = out$jointseg
if (!plotX) mat = subset(mat, chrom < 23)
mat = get.cumulative.chr.maploc(mat, load.genome)
mid = mat$mid[names(mat$mid) %in% mat$mat$chrom]
mat = mat$mat
cncf = fit$cncf
if (!plotX) cncf = subset(cncf, chrom < 23)
dipLogR = out$dipLogR
cnlr.median = rep(cncf$cnlr.median, cncf$num.mark)
mat = cbind(mat, cnlr.median)
starts = cumsum(c(1,cncf$num.mark))[1:length(cncf$num.mark)]
ends = cumsum(c(cncf$num.mark))
my.starts = mat[starts,c('chr.maploc','cnlr.median')]
my.ends = mat[ends,c('chr.maploc','cnlr.median')]
if(is.null(sample.num)){subset_ = 1:nrow(mat)}
if(is.null(sample.num) == FALSE){
if(sample.num >= nrow(mat)){subset_ = 1:nrow(mat)}
if(sample.num < nrow(mat)){subset_ = sort(sample(1:nrow(mat), sample.num, replace=FALSE))}
}
if (!is.null(subset.indices)) {
mat = mat[subset.indices,]
} else { mat = mat[subset_,] }
col.rep = 1 + rep(mat$chrom - 2 * floor(mat$chrom/2))
pt.cols = c(col.1, col.2)[col.rep]
ymin = floor(min(range(cncf$cnlr.median), na.rm = T))
if (ymin > -3) ymin = -3
cnlr = ggplot(mat, environment = environment())
if(!is.null(gene.pos)){
cnlr = cnlr + geom_vline(xintercept=gene.pos$mid, color='palevioletred1')
mat$gene = FALSE
mat$gene[which(mat$chrom == gene.pos$chrom & mat$maploc >= gene.pos$start & mat$maploc <= gene.pos$end)] = TRUE
} else { mat$gene = FALSE }
cnlr = cnlr +
#geom_point(aes(y=cnlr,x=chr.maploc), colour=pt.cols, size=.4) +
geom_point(aes(y = cnlr, x = chr.maploc), pch = 19, col = pt.cols, size = .4) +
geom_point(data = subset(mat, gene == T), aes(y = cnlr, x = chr.maploc), color = '#525252', size = .4) +
scale_x_continuous(breaks=mid, labels=names(mid)) +
xlab('') +
scale_y_continuous(breaks = scales::pretty_breaks(), limits = c(ymin ,3)) +
ylab('Copy number log ratio') +
geom_hline(yintercept = dipLogR, color = 'sandybrown', size = .8) +
geom_segment(data=cncf,aes(x=my.starts$chr.maploc, xend=my.ends$chr.maploc, y=my.starts$cnlr.median, yend=my.ends$cnlr.median), col='red3', size=1, lineend=lend)
panel.grid.col='white'; grid.width = .5
if(theme=='bw'){panel.grid.col='grey'; grid.width = .2; cnlr = cnlr + theme_bw()}
cnlr = cnlr + theme(axis.text.x = element_text(angle = 0, size = 8),
axis.text.y = element_text(angle = 0, size = 8),
text = element_text(size = 10),
panel.grid.minor.x = element_line(colour = panel.grid.col, size = grid.width),
panel.grid.major.x = element_line(colour = panel.grid.col, size = 0),
plot.margin = unit(c(0, 1, 0, 0), 'lines'))
cnlr
}
var.allele.log.odds.ratio = function(out, fit, load.genome=FALSE, gene.pos=NULL, col.1="#0080FF", col.2="#4CC4FF", sample.num=NULL, lend='butt', theme='bw', subset.indices = NULL, plotX = FALSE){
mat = out$jointseg
if (!plotX) mat = subset(mat, chrom < 23)
mat = get.cumulative.chr.maploc(mat, load.genome)
mid = mat$mid
mat = mat$mat
cncf = fit$cncf
if (!plotX) cncf = subset(cncf, chrom < 23)
mafR = rep(sqrt(abs(cncf$mafR)), cncf$num.mark)
mat = cbind(mat, mafR)
starts = cumsum(c(1,cncf$num.mark))[1:length(cncf$num.mark)]
ends = cumsum(c(cncf$num.mark))
my.starts = mat[starts,c('chr.maploc','mafR')]
my.ends = mat[ends,c('chr.maploc','mafR')]
if(is.null(sample.num)){subset_ = 1:nrow(mat)}
if(is.null(sample.num) == FALSE){
if(sample.num >= nrow(mat)){subset_ = 1:nrow(mat)}
if(sample.num < nrow(mat)){subset_ = sort(sample(1:nrow(mat), sample.num, replace=FALSE))}
}
if (!is.null(subset.indices)) {
mat = mat[subset.indices,]
} else { mat = mat[subset_,] }
col.rep = 1 + rep(mat$chrom - 2 * floor(mat$chrom/2))
pt.cols = c(col.1, col.2)[col.rep]
valor = ggplot(mat, environment = environment())
if(!is.null(gene.pos)){
valor = valor + geom_vline(xintercept=gene.pos$mid, color='palevioletred1')
mat$gene = FALSE
mat$gene[which(mat$chrom == gene.pos$chrom & mat$maploc >= gene.pos$start & mat$maploc <= gene.pos$end)] = TRUE
} else { mat$gene = FALSE }
valor = valor +
geom_point(aes(y=valor,x=chr.maploc), colour=pt.cols, size=.4) +
geom_point(data = subset(mat, gene==T), aes(y=valor,x=chr.maploc), color='#525252', size=.4) +
scale_x_continuous(breaks=mid, labels=names(mid)) +
xlab('') +
ylim(-4,4) +
ylab('Variant allele log odds ratio') +
geom_segment(data=cncf, aes(x=my.starts$chr.maploc, xend=my.ends$chr.maploc, yend=my.ends$mafR, y=my.starts$mafR), col='red3', size=1, lineend=lend) +
geom_segment(data=cncf, aes(x=my.starts$chr.maploc, xend=my.ends$chr.maploc, yend=-my.ends$mafR, y=-my.starts$mafR), col='red3', size=1, lineend=lend)
panel.grid.col='white'; grid.width = .5
if(theme=='bw'){panel.grid.col='grey'; grid.width = .2; valor = valor + theme_bw()}
valor = valor + theme(axis.text.x = element_text(angle=0, size=8),
axis.text.y = element_text(angle=0, size=8),
text = element_text(size=10),
panel.grid.minor.x=element_line(colour=panel.grid.col, size=grid.width),
panel.grid.major.x=element_line(colour=panel.grid.col, size=0),
plot.margin = unit(c(0,1,0,0), 'lines'))
valor
}
cellular.fraction = function(out, fit, method=c('cncf', 'em'), load.genome=FALSE, gene.pos=NULL, main='', lend='butt', theme='bw', plotX = FALSE, ...){
mat = out$jointseg
if (!plotX) mat = subset(mat, chrom < 23)
mat = get.cumulative.chr.maploc(mat, load.genome)
mid = mat$mid
mat = mat$mat
cncf = fit$cncf
if (!plotX) cncf = subset(cncf, chrom < 23)
if(method == 'em'){cncf$cf.em[is.na(cncf$cf.em)] = -1; cf = rep(cncf$cf.em, cncf$num.mark); my.ylab='Cellular fraction (EM)'}
if(method == 'cncf'){cncf$cf[is.na(cncf$cf)] = -1; cf = rep(cncf$cf, cncf$num.mark); my.ylab='Cellular fraction (CNCF)'}
mat = cbind(mat, cf)
starts = cumsum(c(1,cncf$num.mark))[1:length(cncf$num.mark)]
ends = cumsum(c(cncf$num.mark))
my.starts = mat[starts,c('chr.maploc','cf')]
my.ends = mat[ends,c('chr.maploc','cf')]
cf = ggplot(mat, environment = environment())
if(!is.null(gene.pos)){
cf = cf + geom_vline(xintercept=gene.pos$mid, color='palevioletred1')
}
cf = cf +
geom_segment(data=cncf, aes(x=my.starts$chr.maploc, xend=my.ends$chr.maploc, yend=my.ends$cf, y=my.starts$cf), col='black', size=1, lineend=lend) +
scale_x_continuous(breaks=mid, labels=names(mid)) +
xlab('') +
ylim(0,1) +
ylab(my.ylab)
panel.grid.col='white'; grid.width = .5
if(theme=='bw'){panel.grid.col='grey'; grid.width = .2; cf = cf + theme_bw()}
cf = cf + theme(axis.text.x = element_text(angle=90, vjust=0, size=8),
axis.text.y = element_text(angle=90, vjust=0, size=8),
text = element_text(size=10),
panel.grid.minor.x=element_line(colour=panel.grid.col, size=grid.width),
panel.grid.major.x=element_line(colour=panel.grid.col, size=0),
plot.margin = unit(c(0,1,0,0), 'lines'))
cf
}
integer.copy.number = function(out, fit, method=c('cncf', 'em'), load.genome=FALSE, gene.pos=NULL, main='', lend='butt', theme='bw', plotX = FALSE, ...){
mat = out$jointseg
if (!plotX) mat = subset(mat, chrom < 23)
mat = get.cumulative.chr.maploc(mat, load.genome)
mid = mat$mid
mat = mat$mat
cncf = fit$cncf
if (!plotX) cncf = subset(cncf, chrom < 23)
if(method == 'em'){tcnscaled = cncf$tcn.em; tcnscaled[cncf$tcn.em > 5 & !is.na(cncf$tcn.em)] = (5 + (tcnscaled[cncf$tcn.em > 5 & !is.na(cncf$tcn.em)] - 5)/3)}
if(method == 'cncf'){tcnscaled = cncf$tcn; tcnscaled[cncf$tcn > 5 & !is.na(cncf$tcn)] = (5 + (tcnscaled[cncf$tcn > 5 & !is.na(cncf$tcn)] - 5)/3)}
tcn_ = rep(tcnscaled, cncf$num.mark)
if(method == 'em'){lcn_ = rep(cncf$lcn.em, cncf$num.mark); my.ylab='Integer copy number (EM)'}
if(method == 'cncf'){lcn_ = rep(cncf$lcn, cncf$num.mark); my.ylab='Integer copy number (CNCF)'}
mat = cbind(mat, cbind(tcn_, lcn_))
starts = cumsum(c(1,cncf$num.mark))[1:length(cncf$num.mark)]
ends = cumsum(c(cncf$num.mark))
my.tcn.starts = mat[starts,c('chr.maploc','tcn_')]
my.tcn.ends = mat[ends,c('chr.maploc','tcn_')]
my.lcn.starts = mat[starts,c('chr.maploc','lcn_')]
my.lcn.ends = mat[ends,c('chr.maploc','lcn_')]
icn = ggplot(mat, environment = environment())
if(!is.null(gene.pos)){
icn = icn + geom_vline(xintercept=gene.pos$mid, color='palevioletred1')
}
icn = icn +
geom_segment(data=cncf, aes(x=my.lcn.starts$chr.maploc, xend=my.lcn.ends$chr.maploc, y=my.lcn.starts$lcn_, yend=my.lcn.ends$lcn_), col='red', size=1, lineend=lend) +
geom_segment(data=cncf, aes(x=my.tcn.starts$chr.maploc, xend=my.tcn.ends$chr.maploc, y=my.tcn.starts$tcn_, yend=my.tcn.ends$tcn_), col='black', size=1,lineend=lend) +
scale_y_continuous(breaks=c(0:5, 5 + (1:35)/3), labels=0:40,limits = c(0, NA)) +
scale_x_continuous(breaks=mid, labels=names(mid)) +
ylab(my.ylab) +
xlab('')
panel.grid.col='white'; grid.width = .5
if(theme=='bw'){panel.grid.col='grey'; grid.width = .2; icn = icn + theme_bw()}
icn = icn + theme(axis.text.x = element_text(angle=0, size=8),
axis.text.y = element_text(angle=0, size=8),
text = element_text(size=10),
panel.grid.minor.x=element_line(colour=panel.grid.col, size=grid.width),
panel.grid.major.x=element_line(colour=panel.grid.col, size=0),
plot.margin = unit(c(0,1,0,0), 'lines'))
icn
}
clonal.cluster = function(out, fit, method='em', load.genome=FALSE, gene.pos=NULL, main='', theme='bw', plotX = FALSE) {
mat = out$jointseg
if (!plotX) mat = subset(mat, chrom < 23)
mat = get.cumulative.chr.maploc(mat, load.genome)
mid = mat$mid
mat = mat$mat
cncf = fit$cncf
if (!plotX) mat = subset(mat, chrom < 23)
if(method == 'em'){tcnscaled = cncf$tcn.em; tcnscaled[cncf$tcn.em > 5 & !is.na(cncf$tcn.em)] = (5 + (tcnscaled[cncf$tcn.em > 5 & !is.na(cncf$tcn.em)] - 5)/3)}
if(method == 'cncf'){tcnscaled = cncf$tcn; tcnscaled[cncf$tcn > 5 & !is.na(cncf$tcn)] = (5 + (tcnscaled[cncf$tcn > 5 & !is.na(cncf$tcn)] - 5)/3)}
tcn_ = rep(tcnscaled, cncf$num.mark)
if(method == 'em'){lcn_ = rep(cncf$lcn.em, cncf$num.mark); my.ylab='CF EM'}
if(method == 'cncf'){lcn_ = rep(cncf$lcn, cncf$num.mark); my.ylab='Integer copy number (CNCF)'}
mat = cbind(mat, cbind(tcn_, lcn_))
starts = cumsum(c(1,cncf$num.mark))[1:length(cncf$num.mark)]
ends = cumsum(c(cncf$num.mark))
my.tcn.starts = mat[starts,c('chr.maploc','tcn_')]
my.tcn.ends = mat[ends,c('chr.maploc','tcn_')]
my.lcn.starts = mat[starts,c('chr.maploc','lcn_')]
my.lcn.ends = mat[ends,c('chr.maploc','lcn_')]
ccl = ggplot(mat, environment = environment())
if(!is.null(gene.pos)){
ccl = ccl + geom_vline(xintercept=gene.pos, color='palevioletred1')
}
ccf.col = c(colorRampPalette(c("white", "steelblue"))(10),"bisque2")[round(10*cncf$cf.em+0.501)]
ccl = ccl +
geom_rect(data=cncf, aes(xmin=my.tcn.starts$chr.maploc, xmax=my.tcn.ends$chr.maploc, ymax=1, ymin=0), fill=ccf.col, col = 'white', size=0) +
scale_y_continuous(expand = c(0,0)) +
scale_x_continuous(breaks=mid, labels=names(mid)) +
ylab(my.ylab) +
xlab('')
panel.grid.col='white'; grid.width = .5
if(theme=='bw'){panel.grid.col='grey'; grid.width = .2; ccl = ccl + theme_bw()}
ccl = ccl + theme(axis.text.x = element_text(angle=90, vjust=0, size=8),
axis.text.y = element_text(angle=90, vjust=0, size=8, color = 'white'),
axis.ticks.y = element_line(color = 'white'),
text = element_text(size=10),
panel.grid.major.y = element_blank(),
panel.grid.minor.y = element_blank(),
panel.grid.major.x= element_blank(),
panel.grid.minor.x= element_blank(),
plot.margin = unit(c(0,1,0,0), 'lines'))
ccl
}
get.cumulative.chr.maploc = function(mat, load.genome=FALSE){
if(load.genome){
require(BSgenome.Hsapiens.UCSC.hg19)
genome = BSgenome.Hsapiens.UCSC.hg19
chrom.lengths = seqlengths(genome)[1:23]
}
else{
chrom.lengths = c(249250621, 243199373, 198022430, 191154276, 180915260, 171115067, 159138663, 146364022, 141213431, 135534747,135006516,
133851895, 115169878, 107349540, 102531392, 90354753, 81195210, 78077248, 59128983, 63025520, 48129895, 51304566, 155270560) #hg19
}
cum.chrom.lengths = cumsum(as.numeric(chrom.lengths))
mid = cum.chrom.lengths - (chrom.lengths/2)
names(mid) = 1:23
chr.maploc.to.gen.maploc = function(x){mat[mat$chrom==x,]$maploc + cum.chrom.lengths[x-1]}
chr.maploc = sapply(2:23,chr.maploc.to.gen.maploc)
chr.maploc = unlist(chr.maploc)
chr.maploc = c(mat[mat$chrom==1,]$maploc,chr.maploc)
mat = cbind(mat,chr.maploc)
list(mat=mat, mid=mid)
}
get.gene.pos = function(hugo.symbol,my.path=paste0(getSDIR(),'/Homo_sapiens.GRCh37.75.canonical_exons.bed'),load.genome=FALSE,genome="hg19"){
if(genome=='hg38'){
require(BSgenome.Hsapiens.UCSC.hg38)
genome <- BSgenome.Hsapiens.UCSC.hg38
chrom.lengths = seqlengths(genome)[1:23]
}
else{
chrom.lengths = c(249250621, 243199373, 198022430, 191154276, 180915260, 171115067, 159138663, 146364022, 141213431, 135534747, 135006516,
133851895, 115169878, 107349540, 102531392, 90354753, 81195210, 78077248, 59128983, 63025520, 48129895, 51304566, 155270560) #hg19
}
cum.chrom.lengths = cumsum(as.numeric(chrom.lengths))
require(rtracklayer)
genes = import.bed(my.path)
mcols(genes)$name = matrix(unlist(strsplit(mcols(genes)$name,':')),nc=3,byrow=T)[,1]
ldply(hugo.symbol, function(x) {
gene.start = min(start(genes[which(mcols(genes)$name == x)]))
gene.end = max(end(genes[which(mcols(genes)$name == x)]))
mid.point = gene.start + ((gene.end - gene.start)/2)
chrom = seqnames(genes[which(mcols(genes)$name == x)])[1]
if (as.character(chrom) == 'X') chrom@values = 23
if (as.integer(chrom)>1) mid.point = cum.chrom.lengths[as.integer(chrom)-1] + mid.point
message(c(mid = mid.point, chrom = as.numeric(as.character(chrom@values)), start = gene.start, end = gene.end))
c(mid = mid.point, chrom = as.numeric(as.character(chrom@values)), start = gene.start, end = gene.end)
})
}
#Standard facets output plot
plot.facets.all.output = function(out, fit, w=850, h=1100, type='png', load.genome=FALSE, main='', plotname='test',
gene.name=NULL, lend='butt', em.plot = FALSE, subset.snps=FALSE) {
if(!is.null(gene.name)){gene.pos = get.gene.pos(gene.name)}
if(is.null(gene.name)){gene.pos = NULL}
if (subset.snps == TRUE) {
subset.indices = random.subset.snps(out$jointseg)
} else { subset.indices = NULL }
if (em.plot == F) {
cnlr = copy.number.log.ratio(out, fit, gene.pos=gene.pos, lend=lend, subset.indices=subset.indices)
valor = var.allele.log.odds.ratio(out, fit, gene.pos=gene.pos, lend=lend, subset.indices=subset.indices)
cfem = cellular.fraction(out, fit, method='em', gene.pos=gene.pos, lend=lend)
cfcncf = cellular.fraction(out, fit, method='cncf', gene.pos=gene.pos, lend=lend)
icnem = integer.copy.number(out, fit, method='em', gene.pos=gene.pos, lend=lend)
icncncf = integer.copy.number(out, fit, method='cncf', gene.pos=gene.pos, lend=lend)
all.plots = list(cnlr, valor, cfem, icnem, cfcncf, icncncf)
plot.no = 6
plot.h = rep(1, 6)
} else {
cnlr = copy.number.log.ratio(out, fit, gene.pos=gene.pos, lend=lend, subset.indices=subset.indices)
valor = var.allele.log.odds.ratio(out, fit, gene.pos=gene.pos, lend=lend, subset.indices=subset.indices)
icnem = integer.copy.number(out, fit, method='em', gene.pos=gene.pos, lend=lend)
cclem = clonal.cluster(out, fit, method = 'em')
icncncf = integer.copy.number(out, fit, method='cncf', gene.pos=gene.pos, lend=lend)
cclcncf = clonal.cluster(out, fit, method = 'cncf')
all.plots = list(cnlr, valor, icnem, cclem, icncncf, cclcncf)
plot.no = 6
plot.h = c(1,1,1,.25,1,.25)
h = (4.5/5)*h
}
if(type == 'pdf'){plotname = paste(plotname, '.pdf', sep=''); CairoPDF(width = 8.854167, height=11.458333, file=plotname)}
if(type == 'png'){plotname = paste(plotname, '.png', sep=''); CairoPNG(width = w, height=h, file=plotname, units='px')}
if(main != ''){grid.arrange(grobs = all.plots,
ncol=1,
nrow=plot.no,
heights = plot.h,
top=textGrob(main))}
if(main == ''){grid.arrange(grobs = all.plots,
ncol=1,
heights = plot.h,
nrow=plot.no)}
dev.off()
}
# Subset SNPs for plots that can be handled by e.g. Illustrator, defaults to 5-fold downsampling at present
random.subset.snps = function(jointseg, by_factor=5) {
chrom.weigths = table(jointseg$chrom)/sum(table(jointseg$chrom))
row.weights = chrom.weigths[match(jointseg$chrom, names(chrom.weigths))]
subset.indices = sort(sample(seq_len(nrow(jointseg)), size = nrow(jointseg)/by_factor, replace = F, prob = row.weights))
subset.indices
}
#Need to add this functionality so it can be callled by the wrapper, doFacets.R etc.
close.up = function(out, fit, chrom.range=NULL, method=NA, gene.name=NULL, lend='butt', bed.path=NULL, subset.snps=FALSE, plotX = FALSE,genome='hg19', ...){
if (!is.null(bed.path)) { gene.info = get.gene.pos(gene.name, my.path = bed.path,genome)
} else { gene.info = get.gene.pos(gene.name) }
if (!is.null(gene.name)) gene.pos = gene.info
if (is.null(gene.name)) gene.pos = NULL
if (is.null(chrom.range)) chrom.range = min(gene.info$chrom):max(gene.info$chrom)
if (23 %in% chrom.range) plotX = TRUE
out$out = out$out[out$out$chrom %in% chrom.range,]
out$jointseg = out$jointseg[out$jointseg$chrom %in% chrom.range,]
out$IGV = out$IGV[out$IGV$chrom %in% chrom.range,]
fit$cncf = fit$cncf[fit$cncf$chrom %in% chrom.range,]
if (subset.snps != FALSE) {
if (subset.snps==T) {
subset.indices = random.subset.snps(out$jointseg)
} else if (is.numeric(subset.snps)) {
subset.indices = random.subset.snps(out$jointseg, subset.snps)
}
} else { subset.indices = NULL }
cnlr = copy.number.log.ratio(out, fit, gene.pos=gene.pos, lend=lend, subset.indices=subset.indices, plotX = plotX, ...)
valor = var.allele.log.odds.ratio(out, fit, gene.pos=gene.pos, lend=lend, subset.indices=subset.indices, plotX = plotX, ...)
output_list <- list(cnlr=cnlr,valor=valor)
if(method == 'em' | is.na(method)){
cfem = cellular.fraction(out, fit, method='em', gene.pos=gene.pos, lend=lend, plotX = plotX, ...)
icnem = integer.copy.number(out, fit, method='em', gene.pos=gene.pos, lend=lend, plotX = plotX, ...)
output_list <- c(output_list, list(cfem=cfem, icnem=icnem))
}
if(method == 'cncf' | is.na(method)){
cfcncf = cellular.fraction(out, fit, method='cncf', gene.pos=gene.pos, lend=lend, plotX = plotX, ...)
icncncf = integer.copy.number(out, fit, method='cncf', gene.pos=gene.pos, lend=lend, plotX = plotX, ...)
output_list <- c(output_list, list(cfcncf=cfcncf, icncncf=icncncf))
}
output_list
}
############################################################################################################################################
############################################################################################################################################
#Example Plot
akt1.close.ups = function(chrom.range = 13:15, gene.name ='AKT1', w=13, h=8, plotname='proj_5513_wxs.pdf',type='pdf',method='cncf'){
load('~/work//AKT1_UCEC//my_r_003//s_TS01_T/s_TS01_T__s_TS01_N/facets_p300c100/s_TS01_T__s_TS01_N_hisens.Rdata')
ts01 = close.up(out, fit, chrom.range=chrom.range, method=method, gene.name=gene.name)
load('~/work//AKT1_UCEC//my_r_003//s_TS02_T/s_TS02_T__s_TS02_N/facets_p300c100/s_TS02_T__s_TS02_N_hisens.Rdata')
ts02 = close.up(out, fit, chrom.range=chrom.range, method=method, gene.name=gene.name)
load('~/work//AKT1_UCEC//my_r_003//s_TS03_T/s_TS03_T__s_TS03_N/facets_p300c100/s_TS03_T__s_TS03_N_hisens.Rdata')
ts03 = close.up(out, fit, chrom.range=chrom.range, method=method, gene.name=gene.name)
load('~/work//AKT1_UCEC//my_r_003//s_TS04_T/s_TS04_T__s_TS04_N/facets_p300c100/s_TS04_T__s_TS04_N_hisens.Rdata')
ts04 = close.up(out, fit, chrom.range=chrom.range, method=method, gene.name=gene.name)
load('~/work//AKT1_UCEC//my_r_003//s_TS05_T/s_TS05_T__s_TS05_N/facets_p300c100/s_TS05_T__s_TS05_N_hisens.Rdata')
ts05 = close.up(out, fit, chrom.range=chrom.range, method=method, gene.name=gene.name)
layout = matrix(1:15, nrow = 3)
if(type == 'pdf'){CairoPDF(width = w, height=h, file=plotname)}
if(type == 'png'){CairoPNG(width = w, height=h, file=plotname, units='px')}
grid.arrange(ts01$cnlr, ts02$cnlr, ts03$cnlr, ts04$cnlr, ts05$cnlr,
ts01$valor, ts02$valor, ts03$valor, ts04$valor, ts05$valor,
ts01$icncncf, ts02$icncncf, ts03$icncncf, ts04$icncncf, ts05$icncncf,
ncol=5, nrow=3)
dev.off()
}
#Example Plots
akt1.wxs = function(){
load('~/work//AKT1_UCEC//my_r_003//s_TS01_T/s_TS01_T__s_TS01_N/facets_p300c100/s_TS01_T__s_TS01_N.Rdata')
#load('/ifs/work/taylorlab/donoghum/AKT1_UCEC//my_r_003//s_TS01_T/s_TS01_T__s_TS01_N/facets_p300c100/s_TS01_T__s_TS01_N_hisens.Rdata')
plot.facets.all.output(out, fit, type='pdf', main='TS01 | cval: 100', plotname='TS01', gene.name='AKT1')
load('~/work//AKT1_UCEC//my_r_003//s_TS02_T/s_TS02_T__s_TS02_N/facets_p300c100/s_TS02_T__s_TS02_N.Rdata')
#load('/ifs/work/taylorlab/donoghum/AKT1_UCEC//my_r_003//s_TS02_T/s_TS02_T__s_TS02_N/facets_p300c100/s_TS02_T__s_TS02_N.Rdata')
plot.facets.all.output(out, fit, type='pdf', main='TS02 | cval: 100', plotname='TS02', gene.name='AKT1')
load('~/work//AKT1_UCEC//my_r_003//s_TS03_T/s_TS03_T__s_TS03_N/facets_p300c100/s_TS03_T__s_TS03_N.Rdata')
#load('/ifs/work/taylorlab/donoghum/AKT1_UCEC//my_r_003//s_TS03_T/s_TS03_T__s_TS03_N/facets_p300c100/s_TS03_T__s_TS03_N.Rdata')
plot.facets.all.output(out, fit, type='pdf', main='TS03 | cval: 100', plotname='TS03', gene.name='AKT1')
load('~/work//AKT1_UCEC//my_r_003//s_TS04_T/s_TS04_T__s_TS04_N/facets_p300c100/s_TS04_T__s_TS04_N.Rdata')
#load('/ifs/work/taylorlab/donoghum/AKT1_UCEC//my_r_003//s_TS04_T/s_TS04_T__s_TS04_N/facets_p300c100/s_TS04_T__s_TS04_N.Rdata')
plot.facets.all.output(out, fit, type='pdf', main='TS04 | cval: 100', plotname='TS04', gene.name='AKT1')
load('~/work//AKT1_UCEC//my_r_003//s_TS05_T/s_TS05_T__s_TS05_N/facets_p300c100/s_TS05_T__s_TS05_N.Rdata')
#load('/ifs/work/taylorlab/donoghum/AKT1_UCEC//my_r_003//s_TS05_T/s_TS05_T__s_TS05_N/facets_p300c100/s_TS05_T__s_TS05_N.Rdata')
plot.facets.all.output(out, fit, type='pdf', main='TS05 | cval: 100', plotname='TS05', gene.name='AKT1')
}