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alasccaCNA.R
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#!/usr/bin/env Rscript
###########################################
# check command line
library(getopt)
#long, short, argmask, datatype, desk
#argmask 0=no arg, 1=req, 2=optional
args <- rbind(c("cnr", "b", 1, "character", "bin file from CNVkit"),
c("cns", "s", 1, "character", "segment file from CNVkit"),
c("germlinevcf", "g", 1, "character", "germline variant vcf file"),
c("somaticvcf", "m", 1, "character", "somatic variant vcf file"),
c("png", "p", 1, "character", "output png file"),
c("json.cna", "o", 1, "character", "CNA output json file"),
c("json.purity", "t", 1, "character", "purity output json file"),
#c("genes.genePred", "g", 1, "character", "ensemble genePred file"),
c("chrsizes", "c", 1, "character", "chromosome sizes file")#,
#c("snptable", "x", 2, "character", "SNP table Rdata file")
)
opts <- getopt(args)
if(is.null(opts$cnr)){
stop("Must specify input bin file --cnr/-b.")
}
if(is.null(opts$cns)){
stop("Must specify input segment file --cns/-s.")
}
if(is.null(opts$germlinevcf)){
stop("Must specify germline vcf file --germlinevcf/-g.")
}
if(is.null(opts$somaticvcf)){
stop("Must specify somatic vcf file --somaticvcf/-m.")
}
if(is.null(opts$png)){
stop("Must specify output png file name --png/-p.")
}
if(is.null(opts$json.cna)){
stop("Must specify output CNA json file name --json.cna/-o")
}
if(is.null(opts$json.purity)){
stop("Must specify output purity json file name --json.purity/-t")
}
if(is.null(opts$chrsizes)){
stop("Must specify chrsizes file --chrsizes/-c.")
}
##############################
###### ######## ######## ## ## ########
## ## ## ## ## ## ## ##
## ## ## ## ## ## ##
###### ###### ## ## ## ########
## ## ## ## ## ##
## ## ## ## ## ## ##
###### ######## ## ####### ##
library(data.table)
library(VariantAnnotation)
library(RJSONIO)
writeJson=TRUE
## Settings / Thresholds
# PTEN copy number
min_tcov_to_report_PTEN_cna=25
# Purity estimates
min_AF_to_use_for_purity=0.04
min_muts_to_estimate_purity=3
median_AF_req_for_ok_purity=0.10
# ## Load snp positions used to indicate contamination (snpTable)
# if (exists('opts$snptable')) load(opts$snptable)
# if (!exists('snpTable')) try( {
# load('/nfs(home/marmay/snpTable.Rdata')
# }, silent=T)
## Read and modify chromosome lengths for plot
chrsizes <- fread(opts$chrsizes)
setnames(chrsizes, names(chrsizes), c("chr", "size"))
chrsizes$cumend <- cumsum( as.numeric( chrsizes$size) )
chrsizes$cumstart <- as.numeric(chrsizes$cumend) - as.numeric(chrsizes$size)
chrsizes$labelpos <- cumsum( as.numeric(chrsizes$size)) - chrsizes$size/2
mids_chrnames <- c(1:22, 'X', 'Y', 'MT')
mids_list <- c(1.25e+08, 93300000, 9.1e+07, 50400000, 48400000, 6.1e+07, 59900000,
45600000, 4.9e+07, 40200000, 53700000, 35800000, 17900000, 17600000,
1.9e+07, 36600000, 2.4e+07, 17200000, 26500000, 27500000, 13200000,
14700000, 60600000, 12500000,NA)
chrsizes$mid <- mids_list[match(chrsizes$chr, mids_chrnames)]
## Load refseq genes
# load('~/Dropbox/refseq.genes.Rdata')
# genes=data.frame(label=as.character(refseq.genelist$name2),chromosome=substr(refseq.genelist$chrom,4,6),start=refseq.genelist$txStart,end=refseq.genelist$txEnd,stringsAsFactors = F)
# genes=genes[order(genes$label),]
# genes=genes[genes$label %in% c('BRAF','KRAS','NRAS','PIK3CA','PIK3R1','PTEN','IGF2','APC','TP53','MLH1','MLH3','MSH2','MSH3','MSH6','PMS2'),]
# d=genes$label[-1]==rev(rev(genes$label)[-1])
# genes=genes[c(T,!d),]
genes=structure(list(label = c("APC", "BRAF", "IGF2", "KRAS", "MLH1",
"MLH3", "MSH2", "MSH3", "MSH6", "NRAS", "PIK3CA", "PIK3R1", "PMS2",
"PTEN", "TP53"), chromosome = c("5", "7", "11", "12", "3", "14",
"2", "5", "2", "1", "3", "5", "7", "10", "17"), start = c(112043201L,
140433812L, 2150346L, 25358179L, 37034840L, 75480466L, 47630205L,
79950466L, 48010220L, 115247084L, 178866310L, 67584251L, 6012869L,
89623194L, 7571719L), end = c(112181936L, 140624564L, 2160204L,
25403854L, 37092337L, 75518235L, 47710367L, 80172634L, 48034092L,
115259515L, 178952497L, 67597649L, 6048737L, 89728532L, 7590868L
)), .Names = c("label", "chromosome", "start", "end"), row.names = c(11203L,
14795L, 24708L, 27510L, 7970L, 29362L, 4622L, 11150L, 5591L,
2723L, 7658L, 11731L, 14834L, 22654L, 34523L), class = "data.frame")
genes=genes[genes$label %in% c("APC", "BRAF", "KRAS","NRAS", "PIK3CA", "PIK3R1","PTEN","TP53"),]
genes$cumstart <- genes$start + chrsizes$cumstart[match(genes$chromosome,chrsizes$chr)]
genes$cumend <- genes$end + chrsizes$cumstart[match(genes$chromosome,chrsizes$chr)]
## PTEN exons hard coded
pten=structure(list(start = c(89623194, 89623861, 89653781, 89685269,
89690802, 89692769, 89711874, 89717609, 89720650, 89725043),
end = c(89623860, 89624305, 89653866, 89685314, 89690846,
89693008, 89712016, 89717776, 89720875, 89731687)),
.Names = c("start", "end"),
row.names = c(NA, -10L), class = "data.frame")
# igf2=structure(list(start = c(2170355, 2168795, 2156596, 2154746, 2150341),
# end = c(2170833, 2169037, 2156759, 2154895, 2154453)),
# .Names = c("start", "end"),
# class = "data.frame", row.names = c(NA,-5L))
###### ## ## ## ## ## ## #### ########
## ## ### ## ## ## ## ## ## ##
## #### ## ## ## ## ## ## ##
## ## ## ## ## ## ##### ## ##
## ## #### ## ## ## ## ## ##
## ## ## ### ## ## ## ## ## ##
###### ## ## ### ## ## #### ##
## Read cnvKit segments and bins
segments <- fread(opts$cns,stringsAsFactors = F)
bins <- fread(opts$cnr,stringsAsFactors = F)
## Segment start and end pos
segments$cumstart <- NA
segments$cumend <- NA
for(chr in chrsizes$chr){
idx <- which( segments$chr == chr )
cumchrsize <- chrsizes$cumend[which(chrsizes$chr == chr)] -
chrsizes$size[which(chrsizes$chr == chr)]
segments$cumstart[idx] <- segments$start[idx] + cumchrsize
segments$cumend[idx] <- segments$end[idx] + cumchrsize
}
segments$centerpos <- segments$cumstart+(segments$cumend-segments$cumstart)/2
## Bin start and end pos
bins$cumstart <- NA
bins$cumend <- NA
for(chr in chrsizes$chr){
idx <- which( bins$chr == chr )
cumchrsize <- chrsizes$cumend[which(chrsizes$chr == chr)] -
chrsizes$size[which(chrsizes$chr == chr)]
bins$cumstart[idx] <- bins$start[idx] + cumchrsize
bins$cumend[idx] <- bins$end[idx] + cumchrsize
}
bins$centerpos <- bins$cumstart+(bins$cumend-bins$cumstart)/2
# Adjust target and antitarget bin log2 ratio so both their medians =0:
ix=bins$gene=='Background'
bins$log2[ix]=bins$log2[ix]-median(bins$log2[ix],na.rm=T)
bins$log2[!ix]=bins$log2[!ix]-median(bins$log2[!ix],na.rm=T)
# Segment values recalculated from the adjusted log2 ratio
bins$segmented <- NA
for (i in 1:nrow(segments)) {
ix=bins$chromosome==segments$chromosome[i] & bins$start>=segments$start[i] & bins$end<=segments$end[i]
s=median(x = bins$log2[ix],na.rm=T)
bins$segmented[ix] <- segments$log2[i] <- s
}
bins$pch=1
bins$pch[bins$gene=='Background']=16
bins$col='black'
bins$col[bins$gene=='Background']='grey'
###### ## ## ######## ######
## ## ### ## ## ## ## ##
## #### ## ## ## ##
###### ## ## ## ######## ######
## ## #### ## ##
## ## ## ### ## ## ##
###### ## ## ## ######
vcf <- readVcf(opts$germlinevcf,genome = "GRCh37")
g <- geno(vcf)
chr <- pos <- rownames(g$DP)
alf=NULL
smoothedAi=NA
if (length(pos)>0) {
for (i in 1:length(pos)) {
temp <- strsplit(chr[i],':')[[1]]
chr[i] <- as.character(temp[1])
pos[i] <- as.numeric(strsplit(temp[2],'_')[[1]][1])
}
pos=as.numeric(pos)
alf <- data.frame(chromosome=chr, start=pos, end=pos, stringsAsFactors = F, cumstart=NA, cumend=NA)
}
if (!is.null(alf)) if( ! all(is.na(alf$chromosome)) ) {
for(chr in chrsizes$chr){
idx <- which(alf$chromosome == chr)
cumchrsize <- chrsizes$cumend[which(chrsizes$chr == chr)] -
chrsizes$size[which(chrsizes$chr == chr)]
alf$cumstart[idx] <- alf$start[idx] + cumchrsize
alf$cumend[idx] <- alf$end[idx] + cumchrsize
}
alf$t <- as.numeric(g$AO[,2])/as.numeric(g$DP[,2])
alf$t[is.nan(alf$t)]=NA # allele freq becomes NaN if cov=0. Then set to NA
alf$n <- as.numeric(g$AO[,1])/as.numeric(g$DP[,1])
alf$n[is.nan(alf$n)]=NA # allele freq becomes NaN if cov=0. Then set to NA
alf$td <- as.numeric(g$DP[,2])
alf$nd <- as.numeric(g$DP[,1])
alf$log2 <- NA
delta <- 3e6; for (i in 1:nrow(alf)) {
ix <- bins$chromosome==alf$chromosome[i] & bins$start>alf$start[i]-delta & bins$end<alf$start[i]+delta
alf$log2[i]=median(bins$log2[ix],na.rm=T)
}
alf$ai=2*abs(alf$t-0.5)
# Prepare smoothed AI
ai=alf$ai
smoothedAi=ai
for (i in 1:length(ai)) {
s=max(1,i-5); e=min(i+5,length(ai))
smoothedAi[i]=median(ai[s:e])
}
segments$ai <- NA; for (i in 1:nrow(segments)) {
ix=alf$chromosome==segments$chromosome[i] & alf$start>=segments$start[i] & alf$end<=segments$end[i]
if (sum(alf$td[ix],na.rm=T)>500) segments$ai[i] <- median(alf$ai[ix])#,w = alf$td[ix])
#alf$ai[ix] <- median(alf$ai[ix])
}
}
## ## ## ## ######## ### ######## #### ####### ## ## ######
### ### ## ## ## ## ## ## ## ## ## ### ## ## ##
#### #### ## ## ## ## ## ## ## ## ## #### ## ##
## ### ## ## ## ## ## ## ## ## ## ## ## ## ## ######
## ## ## ## ## ######### ## ## ## ## ## #### ##
## ## ## ## ## ## ## ## ## ## ## ## ### ## ##
## ## ####### ## ## ## ## #### ####### ## ## ######
vcf2 <- readVcf(opts$somaticvcf,genome = "GRCh37")
g <- geno(vcf2)
salf <- data.frame(); if (length(g)>0) { # if there are any somatic mutations...
chr <- pos <- rownames(g$DP)
for (i in 1:length(pos)) {
temp <- strsplit(chr[i],':')[[1]]
chr[i] <- as.character(temp[1])
pos[i] <- as.numeric(strsplit(temp[2],'_')[[1]][1])
}
pos=as.numeric(pos)
salf <- data.frame(chromosome=chr,start=pos,end=pos,stringsAsFactors = F)
salf$cumstart <- NA
salf$cumend <- NA
for(chr in chrsizes$chr){
idx <- which(salf$chromosome == chr)
cumchrsize <- chrsizes$cumend[which(chrsizes$chr == chr)] -
chrsizes$size[which(chrsizes$chr == chr)]
salf$cumstart[idx] <- salf$start[idx] + cumchrsize
salf$cumend[idx] <- salf$end[idx] + cumchrsize
}
salf$t <- as.numeric(g$VD[,1])/as.numeric(g$DP[,1])
salf$t.altreads <- as.numeric(g$VD[,1])
salf$t.totreads <- as.numeric(g$DP[,1])
salf$n <- as.numeric(g$VD[,2])/as.numeric(g$DP[,2])
salf$n.totreads <- as.numeric(g$DP[,2])
salf$type='other'
salf$type[isSNV(vcf2)]='snv'
salf$type[isDeletion(vcf2)]='del'
salf$type[isInsertion(vcf2)]='ins'
salf$gene=''
salf$consequence=''
salf$aa=''
header=info(header(vcf2))$Description
ix=grep('Consequence annotations from Ensembl',header)
header=c('N',strsplit(header[ix],'\\|')[[1]])
posix=grep('Protein_position',header)
aaix=grep('Amino_acids',header)
consix=grep('Consequence',header)[2]
vep=info(vcf2)$CSQ
alasccaTX=c('ENST00000288602','ENST00000256078','ENST00000369535',
'ENST00000371953','ENST00000521381','ENST00000263967') # alascca genes
#'ENST00000231790','ENST00000380968','ENST00000233146',
#'ENST00000265081','ENST00000234420','ENST00000265849') # MSI genes
## This loop creates a new table "temp" for each mutation.
## (Prev. appended to "table" but "table" was not used further.).
## Unique ALASCCA-relevant effects of each mutation are put in "salf".
#table=NULL
for (i in 1:length(vep)) try( {
temp=NULL
for (j in 1:length(vep[[i]])) { # for each effect
t2=c(i,strsplit(vep[[i]][j],'\\|')[[1]]) # pipe sepatrated line with one effect of the mutation
tpos=strsplit(t2[posix],'/')[[1]][1] # the aa position
taa=strsplit(t2[aaix],'/')[[1]][2] # the aa type
if (!is.na(taa)) { # if any effect on aa
t2=c(t2,paste(tpos, taa,sep='')) # append both together to the vector
temp=rbind(temp,t2) # if any aa effect, add to "temp"
colnames(temp)=header
}
}
if (!is.null(nrow(temp))) {
temp=as.data.frame(temp)
genesymbols=unique(temp$SYMBOL); genesymbols=genesymbols[genesymbols!='']
salf$gene[i]=paste(genesymbols,collapse=',')
salf$consequence[i]=paste(sort(unique(temp[,consix])),collapse=',')
aa=unique(temp[temp$Feature %in% alasccaTX,ncol(temp)]); aa=aa[!aa %in% c('NA','-')]
salf$aa[i]=paste(aa[aa!=''],collapse = ',')
#table=rbind(table,temp)
}
}, silent=T)
#keep <- salf$t>0.05 & salf$n<0.05 & salf$t.altreads>3
# salf <- salf[keep,]
salf$pch=rep(0,nrow(salf))
salf$pch[salf$type=='snv']=1
salf$pch[salf$type=='del']=6
salf$pch[salf$type=='ins']=2
#salf$analysis=n # would crash the script, n was a test environment variable
salf$mut=rownames(g$GT)
} #end somatic mutations
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## Process CNA results to file:
# assemble CNA results first
# Starting with PTEN binned values
marg <- 1e6 # margin btw PTEN and a control region
size <- 7e6 # size of control regions
# select PTEN by coordinate instead of by annotation
#bix <- bins$gene=='PTEN' # PTEN bins
bix <- bins$chromosome == "10" & bins$end > 89622870 & bins$start < 89731687
q10 <- bins$chromosome=='10' & bins$start >= chrsizes$mid[which(chrsizes$chr=='10')] # all 10q bins
# 10q control left of PTEN:
left <- q10 & !bix &
bins$start >= min(bins$start[bix])-marg-size & # start of left control region
bins$end <= min(bins$start[bix])-marg # end of left control region
# 10q control right of PTEN:
right <- q10 & !bix &
bins$start >= max(bins$end[bix])+marg & # start of right control region
bins$end <= max(bins$end[bix])+marg+size # end of right control region
# Also get the CBS segment(s) for PTEN:
# which segments overlap PTEN:
six <- which(segments$chromosome == "10" & segments$end > 89622870 & segments$start < 89731687)
# for each of those (usually one) which bins are inside it:
sbix <- NULL; for (i in 1:length(six)) sbix[[i]] <- which(q10 & bins$start >= segments$start[six[i]] & bins$end <= segments$end[six[i]])
p.left <- wilcox.test(x = bins$log2[bix],y = bins$log2[left],alternative = 'less')$p.value # p value for del relative to left side control
p.right <- wilcox.test(x = bins$log2[bix],y = bins$log2[right],alternative = 'less')$p.value # right control
p.seg.left <- NULL; for (i in 1:length(sbix)) p.seg.left <- c(p.seg.left,wilcox.test(x = bins$log2[sbix[[i]]],y = bins$log2[left],alternative = 'less')$p.value) # PTEN segments to left ctrl
p.seg.right <- NULL; for (i in 1:length(sbix)) p.seg.right <- c(p.seg.right,wilcox.test(x = bins$log2[sbix[[i]]],y = bins$log2[right],alternative = 'less')$p.value) # PTEN segments to left ctrl
m.pten <- median(bins$log2[bix],na.rm = T)
m.left <- median(bins$log2[bix],na.rm = T)-median(bins$log2[left],na.rm = T)
m.right <- median(bins$log2[bix],na.rm = T)-median(bins$log2[right],na.rm = T)
m.seg.left <- NULL; for (i in 1:length(sbix)) m.seg.left <- c(m.seg.left,median(bins$log2[sbix[[i]]],na.rm = T)-median(bins$log2[left],na.rm = T))
m.seg.right <- NULL; for (i in 1:length(sbix)) m.seg.right <- c(m.seg.right,median(bins$log2[sbix[[i]]],na.rm = T)-median(bins$log2[right],na.rm = T))
l.seg <- segments$end[six]-segments$start[six]
# PTEN homozygous deletion called if:
# Whole PTEN or a small (<5M) PTEN-containing CBS segment is sign. below both left & right controls, with absolute logratio diff at least 0.2 (for 13% "ratio" drop in DNA, 25% typical purity and delta50%)
# Thresholds: 0.001 -> base false positive rate of <1/1000 per control (lower due to two tests). With 5% TP, FDR<<2% as amplitude cutoff is also applied.
significance.threshold <- 1e-3
difference.threshold <- -0.2
all.pten.hom.loss <-
p.left < significance.threshold &
p.right < significance.threshold &
m.left < difference.threshold &
m.right < difference.threshold
seg.pten.hom.loss <- FALSE; if (l.seg < 5e6) for (i in 1:length(l.seg)) {
seg.pten.hom.loss <-
seg.pten.hom.loss |
p.seg.left[i] < significance.threshold &
p.seg.right[i] < significance.threshold &
m.left[i] < difference.threshold &
m.right[i] < difference.threshold
}
pten.hom.loss <- all.pten.hom.loss | seg.pten.hom.loss
# PTEN hemizygous loss and LOH:
six <- grep('ENSG00000171862',segments$gene); #seg(s) with PTEN
snps.near <- alf[alf$chromosome==10 & alf$start>=80e6 & alf$end<=100e6,]
snps.ptenseg <- alf[alf$chromosome==10 & alf$start>=min(segments$start[six]) & alf$end<=min(segments$end[six]),]
## call LOH if PTEN segment or vicinity AI is above 0.5 (corr to 50% cells) and with logr below .1
pten.loh <- FALSE
pten.loss <- FALSE
# If there's no SNP table, does not call pten loh or loss
if (!is.null(alf)) {
# Call LOH if logratio is not too high (0.15 max) and AI is above 0.4
pten.loh <-
m.pten < 0.15 & (
median(snps.near$ai,na.rm = T) > 0.4 |
median(snps.ptenseg$ai,na.rm = T) > 0.4
)
## call "LOSS" if PTEN logratio is below -0.25 (some 15% drop in DNA, corr to 30% of cells) and AI>.2
pten.loss <-
m.pten < -0.25 & (
median(snps.near$ai,na.rm = T) > 0.2 |
median(snps.ptenseg$ai,na.rm = T) > 0.2
)
}
if (is.na(pten.loss)) pten.loss=FALSE
if (is.na(pten.loh)) pten.loh=FALSE
# set PTEN call for Json file
ptencall='NOCALL'
if (pten.loh|pten.loss) ptencall='HETLOSS_or_LOH'
if (seg.pten.hom.loss|pten.hom.loss) ptencall='HOMLOSS'
# but if tumor coverage is bad (<min_tcov_to_report_PTEN_cna), report NOCALL regardless:
if (!is.null(alf)) if (median(alf$td,na.rm=T)<min_tcov_to_report_PTEN_cna) ptencall='NOCALL'
# List to put in Json file
CNAlist=list(name='PTEN', call=ptencall, ENSG='ENSG00000171862', ENST='ENST00000371953')
# write to JSON
exportJson <- toJSON(CNAlist) #<--------- This json is only CNA
if (writeJson) write(exportJson, opts$json.cna)
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# purity estimate from mutation allele frequencies
purity.call='OK'; af=NA
## first, require good T coverage (≥100) to consider calling purity FAIL
t=0; try( {# median coverage of at least 1000 snps
if (nrow(alf)>1000)
t=median(alf$td,na.rm=T)
}, silent=T)
if (t>=100) try( { # if ok coverage call FAIL if
ix=salf$t>=min_AF_to_use_for_purity & salf$t.altreads>=6 # use only min_AF_to_use_for_purity+ mutations. also require 6+ reads.
if (sum(alf$chromosome %in% c('X','Y'))<50) ix = ix & !salf$chromosome %in% c('X','Y') # male X/Y mutations come at higher AF, so exclude from calculation if male
af=salf$t[ix]
if (length(af)>=min_muts_to_estimate_purity) {
if (median(af) < median_AF_req_for_ok_purity) {
purity.call='FAIL' # will call fail if AF is too low
}
} else purity.call='FAIL' # and will call fail if the number of muts is too low
},silent=T)
# sometimes CNAs may also indicate ok purity. if OK let it overrule the mutation-based (purity.call).
## first, require at least 2 automsomal segments ≥ 10MB and ≥10% shift in DNA abundance
##-from genome median (equiv to 20% purity and near diploid) (simplified: ±0.2 logratio)
ix=segments$chromosome %in% as.character(1:22) & segments$end-segments$start >= 10e6 & abs(segments$log2) >= 0.2
# cna purity ok if 2+ segments ≥ ±0.15 (and coverage≥100)
cna.purity='LOW/NA'; if (sum(ix)>=2 & t>=100) {
cna.purity='OK'
purity.call='OK'
}
purity=list(
somatic.mutations=length(af[!is.na(af)]),
median.allelefreq=median(af,na.rm = T),
cna.purity=cna.purity,
purity.call=purity.call
)
purity.json=list(
CALL=purity.call
)
# write to JSON
exportJson <- toJSON(purity.json) #<--------- This json is only purity
if (writeJson) write(exportJson, opts$json.purity)
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#Screen configuration overview
#------------------------------------------------------------
#| 5 | |
#-------------------------------| 4 |
#| 6 | |
#------------------------------------------------------------
#| 7 |
#------------------------------------------------------------
#| 8 |
#------------------------------------------------------------
#| 9 |
#------------------------------------------------------------
try( {
png(filename = opts$png,width=11.7,height=8.3,units="in",res=300)
split.screen(figs=c(2,1))
split.screen(as.matrix(data.frame(
left=c(0,0.25),
right=c(0.2,.9635),
bottom=c(0,0.097),
top=c(1,0.903))),1)
split.screen(figs=c(2,1),3)
split.screen(as.matrix(data.frame(left=c(rep(0.015,3)),
right=c(rep(1,3)),
bottom=c(0.10,0.5,0.55),
top=c(0.5,0.55,1))),2)
split.screen(figs=c(4,6),4)
######## ## ####### ######## ###### ######## ######## ######## #### ## ## ###### ######
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## ## ## ## ## ## ## ## ## ## ## #### ## ## ##
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screen(1)
# Sample name and QC
try({
plot(1,type='n',axes=F,xlab='',ylab='')
name=rev(strsplit(opts$cnr,'/')[[1]])[1]
name=strsplit(name,'-panel')[[1]][1]
mtext(name,3,padj=-4.5)
ix=bins$gene=='Background'
snpcov='NA'
if (!is.null(alf)) if (nrow(alf)>0)
snpcov=paste(round(median(alf$td)),round(median(alf$nd)),sep='/')
mapd1=round(median(abs(diff(bins$log2[!ix]))),2)
mapd2=round(median(abs(diff(bins$log2[ix]))),2)
simplePur=round(100*purity$median.allelefreq)
tar=round(median(bins$log2[bins$gene!='Background']),2)
atar=round(median(bins$log2[bins$gene=='Background']),2)
mtext(paste(format(Sys.time(), "%F %H:%M:%S"),'',
' SNPcov:',snpcov,
' MAPD:',paste(mapd1,mapd2,sep='/'),
' Median Somatic AF:',paste(simplePur,'%',sep=''),
' CNA-purity:',purity$cna.purity,
' PurityEst:', purity$purity.call),
3,padj=-4.5,adj=1,cex=0.75)
},silent=T)
cex.axis <- .6
cex.mtext <- 1.5
cex.main <- 2
cex.text <- .7
axis1padj <- -2.35
axis2hadj <- 0.3
text1padj <- 1.7#1.7
text2padj<- -3
padj <- -3
lwd=1
color <- '#00000010'
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screen(5)
#plot snp cov vs alf
xlim=c(1,3000)
ylim=(0:1)
try( {
par(mar=c(2,3,2,.5),xaxs='i',yaxs='i',las=1)
plot(1,type='n',xlim=xlim,ylim=ylim,axes=F,main='Variants',cex.main=0.5,log='x')
text(x=c(2,4,6),y=0.48,labels = c('1 alt','2','3'),cex=0.6,col='grey',srt=-55)
points(x = 7:1000,y=3/(7:1000),type='l',col='grey')
axis(1,c(1,10,100,1000),lwd=lwd,lend=1,cex.axis=cex.axis,padj=axis1padj,tck=-0.03)
axis(2,seq(0,1,.25),lwd=lwd,lend=1,cex.axis=cex.axis,hadj=axis2hadj,tck=-0.03)
mtext('Coverage',1,cex=cex.text,padj=text1padj)
mtext('Alt allele ratio',2,cex=cex.text,las=0,padj=text2padj)
points(alf$td,alf$t,cex=0.3,col='#00000080',xlim=xlim,ylim=ylim,pch=16,lwd=lwd)
points(alf$nd,alf$n,cex=0.1,col='#60606080',xlim=xlim,ylim=ylim,pch=3,lwd=lwd)
scol=rep('#C00000CC',nrow(salf))
scol[salf$t.altreads<6]='#500000CC'
# if (exists('snpTable')) {
# ix=paste(salf[,1],salf[,2]) %in% names(snpTable)[snpTable>10]
# scol[ix]='#0000C0CC'
# }
points(salf$t.totreads,salf$t,cex=0.4,col=scol,xlim=xlim,ylim=ylim,pch=salf$pch,lwd=lwd)
segments(x0=median(alf$td,na.rm = T),y0=0,x1=median(alf$td,na.rm = T),y1=1,col='#00000080',lwd=lwd,lty=3)
segments(x0=median(alf$nd,na.rm = T),y0=0,x1=median(alf$nd,na.rm = T),y1=1,col='#7070FF80',lwd=lwd,lty=3)
segments(y0=median(purity$median.allelefreq,na.rm = T),x0=1,y1=purity$median.allelefreq,y1=1e4,col='red',lwd=lwd,lty=3)
}, silent=T)
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screen(6)
try( {
#plot PTEN only.
xlim=c(89.6e6,89.75e6)
ylim=c(-1.5,0.7)
main='no loss'
if (CNAlist$call=='HETLOSS_or_LOH') main='LOH/loss'
if (CNAlist$call=='HOMLOSS') main='homozygous loss'
main=paste('PTEN:',main)
par(mar=c(2,3,2,.5),xaxs='i',yaxs='i',las=1)
plot(1,type='n',xlim=xlim,ylim=ylim,axes=F,main=main,cex.main=0.5,xaxt='n',yaxt='n',ylab=NA,xlab=NA)
rect(xleft = pten$start,xright = pten$end,ybottom = ylim[1],ytop = ylim[2],col = '#0000C030',border='#0000C030')
segments(
y0=log2(c(.5,1,1.5,2)),
x0=xlim[1],x1=xlim[2],
col='grey',
lwd=1,lty=1)
ix=bins$chromosome==10 & bins$start>xlim[1] & bins$end<xlim[2]
points(x = (bins$start[ix]+bins$end[ix])/2,y = bins$log2[ix],pch=bins$pch[ix],col='black',cex=0.6)
points(x = (bins$start[ix]+bins$end[ix])/2,y = bins$log2[ix],type='l',col='black',lwd=1)
col='#00C000CC'; ix=segments$chromosome=='10'
segments(x0=segments$start[ix],x1=segments$end[ix],
y0=segments$log2[ix],y1=segments$log2[ix],
col=col,
lwd=2)
axis(1,lwd=lwd,lend=1,cex.axis=cex.axis,padj=axis1padj,tck=-0.03)
axis(2,seq(-1,1,0.5),lwd=lwd,lend=1,cex.axis=cex.axis,hadj=axis2hadj,tck=-0.03)
mtext('Log ratio',2,cex=cex.text,las=0,padj=text2padj)
# somatic muts
ix <- salf$chromosome==10
if (sum(ix)>0) {
points(x = salf$start[ix], y = salf$t[ix]-1,pch=salf$pch[ix], cex=0.6, col=scol[ix])
text(x = salf$start[ix],y=-1.25,labels = salf$aa[ix],cex=0.5,srt=90,col=scol[ix])
}
}, silent=T)
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#allelefreq vs logR
screen(4)
try( {
par(mar=c(0,0,0,0))
plot(1,type='n',axes=F,xlab='',ylab='')
mtext('DNA ratio',1,padj=text1padj,cex=cex.text)
mtext('Allelic imablance',2,padj=-3,cex=cex.text)
for (c in 1:24)
{
cex=0.4
screen(c+9) # for chrY, screen is 33.
par(mar = c(0, 0, 0, 0))
par(oma = c(0,0,0,0))
par(mgp =c(1,0.5,0))
#xlim=c(-1.1,1.1)
xlim=c(0.3,2.1)
ylim=c(0,1)
ix <- alf$chromosome %in% c(chrsizes[c]$chr,'X','Y')
ixCurChr <- alf$chromosome %in% chrsizes[c]$chr
plot(2^alf$log2[!ix],smoothedAi[!ix],xlim=xlim,ylim=ylim,lwd=lwd,axes=F,ylab='',xlab='',pch=16,
col='#B0B0B030',cex=cex)
points(2^alf$log2[ixCurChr],smoothedAi[ixCurChr],col='#00800070',pch=16,cex=cex)
if (c==24 & !is.null(alf)) {
d=density(2^alf$log2[alf$chromosome %in% as.numeric(1:22)])
points(d$x,d$y/max(d$y),type='l')
}
whole=(c(0.5,1,1.5,2))
segments(
x0=whole,x1=whole,
y0=-0.05,y1=1.035,
col='#D3D3D360',
lwd=1)
segments(
x0=c(-2),x1=c(5),
y0=c(1/3,1/2),y1=c(1/3,1/2),
col='#D3D3D360',
lwd=1)
if(c == 23)
{
mtext("X", side = 3, line = -1, adj = 0.92, cex = 1)
} else if(c == 24)
{
mtext("", side = 3, line = -1, adj = 0.92, cex = 1)
} else
{
mtext(c, side = 3, line = -1, adj = 0.92, cex = 1)
}
if(c %in% 19:24) axis(side=1,cex.axis=0.5,at=c(0.5,1,1.5,2),tck=-0.05,padj=-1.6,#las=3,
labels=c('-50%','±0','+50%','+100%'),
col='white',col.ticks='black',lend=1)
if(c %in% c(1,7,13,19)) axis(side=2,cex.axis=0.6,tck=-0.05,
at=c(1/3,1/2),
labels=c('2:1','3:1'),
las=1,col='white',col.ticks='black',lend=1)
if(c %in% c(6,12,18,24)) axis(side=4,cex.axis=0.6,tck=-0.05,
at=c(1/3,1/2),
labels=c('2:1','3:1'),
las=1,col='white',col.ticks='black',lend=1)
box(lwd=1)
}
}, silent=T)
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#LogR plot
screen(9)
try( {
#Set marginals, outer marginals and mgp(which is for xlab,ylab,ticks and axis)
par(mar = c(0, 0, 0, 0))
par(oma = c(0,0,0,0))
par(mgp =c(1,0.5,0))
par(lend=1)
ymin = -2
ymax = 2
#Plot signal over complete genome
plot(NA,NA,#mpos[ix],mval[ix],
pch=16,
cex=0.3,
main='',
xlab = "",
ylab = "",
col = '#00000003',
xaxt="n",
axes=F,
ylim = c(ymin,ymax),
xlim = c(0,max(bins$cumend))
)
#Add genes as lines
segments(
x0=(genes$cumstart+genes$cumend)/2,
y0=-100,y1=100,
col='#0000C020',
lwd=2)
#add smoothed signal (OFF)
breaks=c(0,which(abs(diff(bins$start))>1e6),nrow(bins))
# for (i in 2:length(breaks)) {
# ix=(breaks[i-1]+1):breaks[i]
# points((bins$cumstart[ix]+bins$cumend[ix])/2,bins$smoothed[ix],type='l',col='#00C00060',lwd=3)
# }
# real signal (omitted above) on top of it
ix=bins$gene=='Background'
points((bins$cumstart[ix]+bins$cumend[ix])/2,bins$log2[ix], #offtargets
pch=16,
cex=0.6,
#type='l',
col = '#80808080'
)
points((bins$cumstart[!ix]+bins$cumend[!ix])/2,bins$log2[!ix], #ontargets
pch=1,
cex=0.7,
#type='l',
col = '#00000080'
)
seqminmax <- seq(ymin,ymax,by=0.5)
#Add axis to the left & right of signal
axis(side=2,tck=-0.025,at=seqminmax,cex.axis=0.6,pos=0,las=1)
axis(side=4,tck=-0.025,at=seqminmax,cex.axis=0.6,pos=max(bins$cumend),las=1)
mtext("Log ratio",side=2,line=0,cex=0.7,padj=1.15)
whole=(c(.5,1,1.5,2))
#Add grey segments
segments(
y0=log2(whole),y1=log2(whole),
x0=0,x1=max(bins$cumend),
# col='#D3D3D360',
col='#00000020',
lwd=1)
#Add segment for each chromosome.
col='#00C000CC'
segments(x0=segments$cumstart,x1=segments$cumend,
y0=segments$log2,y1=segments$log2,
col=col,
lwd=2)
#Add a bar between chromosomes to distinguish them
segments(
x0=chrsizes$cumend,
y0=-100,y1=100,
col='#00000099',
lwd=1)
}, silent=T)
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#Genes
screen(8)
try( {
#Set marginals, outer marginals and mgp(which is for xlab,ylab,ticks and axis)
par(mar = c(0, 0, 0, 0))
par(oma = c(0,0,0,0))
par(mgp =c(1,0.5,0))
#Create a empty plot with the same ylim and xlim as the plot directly above it (signal) and below (AI)
plot(0,0,xlab="",ylab="",main="",type="n",axes=F,xaxt="n",ylim=c(0,1),xlim=c(0,max(bins$cumend)))
chrsizes$labelpos[chrsizes$chr=='MT']=NA
text(x = chrsizes$labelpos,y = 0.5,labels = chrsizes$chr,cex=.5)
mtext(text='',side=2,las=1,line=-1.2)
# Gene names
text(x = (genes$cumstart+genes$cumend)/2,y = 0.5,labels = genes$label,srt=45,cex=0.4,col='#0000C0CC')
}, silent=T)
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# BAF plot
screen(7)
try( {
#Set marginals, outer marginals and mgp(which is for xlab,ylab,ticks and axis)
par(mar = c(0, 0, 0, 0))
par(oma = c(0,0,0,0))
par(mgp =c(1,0.5,0))
#Plot BAF over whole genome
plot(alf$cumstart,alf$t,
pch=16,
cex=0.6,
cex.axis=1,
main='',
xlab = "",
ylab = "",
col = "#00000080",
xaxt="n",
axes=F,
ylim = c(-0.1,1.1),
xlim = c(0,max(bins$cumend))
)
#Add axis to the left,right and below of AI. The below axis is the chromosome numbers 1-24.
axis(side=2,tck=-0.04,at=seq(0,1,.2),cex.axis=0.6,pos=0,las=1) #at=c(0,0.25,0.33,0.5,0.67,0.75,1),labels=c('0','1/4','1/3','1/2','2/3','3/4','1')
#axis(side=1,at=pre,pos=0,labels=c(seq(from=1,to=22),"X",'Y'),cex.axis=0.50,lty=0)#,tck=0,col.ticks='#00000000')
axis(side=4,tck=-0.04,at=seq(0,1,.2),cex.axis=0.6,pos=max(bins$cumend),las=1) #at=c(0,0.25,0.33,0.5,0.67,0.75,1),labels=c('0','1/4','1/3','1/2','2/3','3/4','1')
mtext("SNP allele ratio",side=2,line=0,cex=0.7,padj = 1.15)
#Add a bar between chromosomes to distinguish them
segments(
x0=chrsizes$cumend,
y0=-100,y1=100,
col='#00000099',
lwd=1)
#Add genes as lines
segments(
x0=(genes$cumstart+genes$cumend)/2,
y0=-100,y1=100,
col='#0000C020',
lwd=2)
segments(
y0=c(0,1/4,1/3,2/3,3/4,1),y1=c(0,1/4,1/3,2/3,3/4,1),
x0=0,x1=max(bins$cumend),
# col='#D3D3D360',
col='#00000020',
lwd=1)
## Add somatic mutations
scol=rep('#C00000CC',nrow(salf))
scol[salf$t.altreads<6]='#500000CC'
# if (exists('snpTable')) {
# ix=paste(salf[,1],salf[,2]) %in% names(snpTable)[snpTable>10]
# scol[ix]='#0000C0CC'
# }
points(salf$cumstart,salf$t,
pch=salf$pch,
cex=0.6,
col=scol
)
if (nrow(salf)>0) text(x = salf$cumstart,y=salf$t-0.07,labels = salf$aa,cex=0.6,srt=30,col=scol)
## Add a line at median AF
segments(x0 = 0,x1 = 3e9,y0=purity$median.allelefreq,y1 = purity$median.allelefreq,col='#C0000099',lty=2)
}, silent=T)
#Close all the opened split.screens and release the figure
close.screen(all.screens=T)
dev.off()
},silent=T)