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CG_Ka_check<- function(chr,start,end,int,ai,Cn,mCn,t,name='',xlim=c(0,2.4),ylim=c(0,1))
{
## TAPS scatter plot of a full sample, used for visual quality control.
png(paste(name,'_Ka_check.png',sep=''),width=1300,height=1300)
colors_p <- colorRampPalette(c("#6600FF","#9900CC"),space="rgb")
colors_q <- colorRampPalette(c("#CC0099","#CC0000"),space="rgb")
#layout(matrix(1:25,nrow=5,byrow=T), widths=1,heights=1)
aix=!(is.na(mCn)|is.na(Cn))
chr=chr[aix]
start=start[aix]
end=end[aix]
int=int[aix]
ai=ai[aix]
Cn=Cn[aix]
mCn=mCn[aix]
pos <- (start+end)/2
length=end-start
labels=paste(Cn,mCn,sep='m')
size=rep(1,length(chr))
size[length>1000000]=2
size[length>3000000]=3
size[length>5000000]=4
size[length>10000000]=5
size[length>20000000]=6
## The variants present in this data
variants <- unique(labels)
variants_data <- NULL
## Extracts the Log-R and Allelic Imbalance Ratio of variants
for (v in variants)
{
t_cn <- paste('cn',strsplit(v,'m')[[1]][1],sep='')
t_int <- as.numeric(t$int[t_cn][[1]])
t_ai <- as.numeric(t$ai[paste('cn',v,sep='')][[1]])
variants_data <- rbind(variants_data,c(t_int,t_ai))
}
col <- rep('#B0B0B070',length(chr))
plot(int,ai,
pch=16,
cex=c(size),
cex.lab=3,
mar=c(0.1,0.1,0.1,0.1),
main = "",
xlab = name,
ylab = "",
col = col,
xlim = xlim,
ylim = ylim,
yaxt="n",
xaxt="n")
axis(1,cex.axis=2)
axis(2,cex.axis=2)
text(variants_data[,1],variants_data[,2],
labels=variants,
col='black',
cex=2)
dev.off()
}
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