use pshannon-bioc / binnedCounts.R and plot the counts for Tumor and Normal Sample Data

plotbinnedCounts.R

plotbinnedCounts <- function(filename, tumor, normal, ylim )
{
    y1 <- tumor
    y2 <- normal
    png(file= filename, bg="white")
    plot( y1,
         col="red",
         xlab= "bins", 
         ylab =" reads",
         ylim =ylim )
    lines( y2, 
          col="darkgreen")
    legend("topright", 
           col=c("red","darkgreen"), 
           legend = c("Tumor", "Normal"),
           text.width = strwidth("1,000,000"),
           lty = 1:2, xjust = 1, yjust = 1,
           title = "Sample Types")
    dev.off()
}

source("binnedCounts.R")
binSize =10000
tumor.bins <- binnedCounts("tumorA.chr4.bam", "chr4", binSize=binSize)
normal.bins <- binnedCounts("normalA.chr4.bam", "chr4", binSize=binSize)

#raw
y1 <- tumor.bins$count
y2 <- normal.bins$count
plotbinnedCounts("count.png", y1, y2,ylim=c(0,max(y1,y2)))

#normalised
normNormal <- normal.bins$count /10000
normTumor <- tumor.bins$count /10000
y1 <- normTumor
y2 <- normNormal
plotbinnedCounts("normalized-count.png", y1, y2,ylim=c(0,2))