This function extracts the raw coverage information calculated by fullCoverage at each base for a set of regions found with calculatePvalues. It can further calculate the mean coverage per sample for each region.
A list where each element is the result from
loadCoverage used with
The total number of reads mapped for each sample.
Providing this data adjusts the coverage to reads in
The target library size to adjust the coverage to. Used
A character vector with the full path to the sample BAM files
(or BigWig files).
The names are used for the column names of the DataFrame. Check
rawFiles for constructing
Arguments passed to other methods and/or advanced arguments. Advanced arguments:
Passed to extendedMapSeqlevels and define_cluster.
fullCov is the output of loadCoverage with
cutoff non-NULL, getRegionCoverage assumes that the regions
come from the same data. Meaning that filterData was not used again.
This ensures that the regions are a subset of the data available in
files is specified, this function
will attempt to read the coverage from the files. Note that if you used
'totalMapped' and 'targetSize' before, you will have to specify them again
to get the same results.
You should use at most one core per chromosome.
a list of data.frame where each data.frame has the coverage
information (nrow = width of region, ncol = number of samples) for a given
region. The names of the list correspond to the region indexes in
Andrew Jaffe, Leonardo Collado-Torres
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## Obtain fullCov object fullCov <- list('21'=genomeDataRaw$coverage) ## Assign chr lengths using hg19 information, use only first two regions library('GenomicRanges') data(hg19Ideogram, package = 'biovizBase', envir = environment()) regions <- genomeRegions$regions[1:2] seqlengths(regions) <- seqlengths(hg19Ideogram)[names(seqlengths(regions))] ## Finally, get the region coverage regionCov <- getRegionCoverage(fullCov=fullCov, regions=regions)
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