Finds genomics regions where the coverage is above a user-specified threshold and counts the number of ranges in each sample overlapping each region.
islandCounts(x, minReads=10, mc.cores=1)
Only regions with coverage above
The output of
islandCounts can be the input data for a number of downstream analysis methods.
Although for a simple-minded analysis one could use
one will usually want to use more sofisticated analyses (e.g. from packages
Object of class
indicating the regions with coverage above
minReads and the number of reads overlapping
each sample for those regions.
signature(x = "RangedData")
x is assumed to contain the reads from a single sample.
Genomic regions with high coverage will be detected and the number of
reads overlapping these regions will be computed.
signature(x = "list")
x is assumed to contain the reads for several samples, one sample
in each element of the list.
The overall coverage across all samples is computed by adding the
coverage in the individual samples, and the regions with overall
coverage above the user-specified threshold are selected.
Then the number of reads overlapping each region is computed.
1 2 3 4 5 6 7 8 9 10 11 12 13 14
set.seed(1) st <- round(rnorm(1000,500,100)) strand <- rep(c('+','-'),each=500) space <- rep('chr1',length(st)) sample1 <- RangedData(IRanges(st,st+38),strand=strand,space=space) st <- round(rnorm(1000,1000,100)) sample2 <- RangedData(IRanges(st,st+38),strand=strand,space=space) regions <- islandCounts(list(sample1,sample2),minReads=50) regions #Plot coverage plot(coverage(sample1)[],type='l',xlim=c(0,2000)) lines(coverage(sample2)[],col=2)
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.