sonali-bioc

library(CNAnorm)
library(RUnit)

#outside R session - convert perl file
##perl bam2windows.pl "tumorA.chr4.bam" "normalA.chr4.bam" > perloutput.txt

test_counts <- function(chr4data)
{
    test_indices <- c(8000001, 8010001,10000001 , 10010001, 1000001)
    test_res <- c(67,62,67,74,47) #from samtools

sonali-bioc

## Our motivation for this gist comes from the fact that we would like
## to check the counts calculated by each of the methods.

## Here we provide a simple unitTest which takes as input a GRanges
## object which contains the reads from the "tumorA.chr4.bam" file.

## we choose 5 regions and get their counts from samtools using a simple
## command :
## samtools view  tumorA.chr4.bam chr4:8000001-8010000   | wc -l      67

sonali-bioc

library(cn.mops)
library(RUnit)

tumor_gr <- getReadCountsFromBAM("tumorA.chr4.bam",
                                 refSeqName="chr4",WL=10000,mode="unpaired")
normal_gr <- getReadCountsFromBAM("normalA.chr4.bam",
                                  refSeqName="chr4",WL=10000,mode="unpaired")

ref_analysis_norm0 <- referencecn.mops(tumor_gr, normal_gr,norm=0) 

sonali-bioc

library(GenomicAlignments)
library(RUnit)
    
cnv_countOverlaps <- 
    function(bamfilename, chrom, binSize=10000)
{
    bf <- BamFile(bamfilename)
    aln<- readGAlignments(bf)
    sub <- resize(granges(aln), width=1)
    tiles <- tileGenome(seqlengths(bf), tilewidth=binSize, 

sonali-bioc

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 )