fullCoverage {derfinder} | R Documentation

## Load the unfiltered coverage information from a group of BAM files and a list of chromosomes

### Description

For a group of samples this function reads the coverage information for several chromosomes directly from the BAM files. Per chromosome, it merges the unfiltered coverage by sample into a DataFrame. The end result is a list with one such DataFrame objects per chromosome.

### Usage

    fullCoverage(files, chrs, bai = NULL, chrlens = NULL, outputs = NULL,
      cutoff = NULL, ...)

### Arguments

`files` | 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 `files`. `files` can also be a `BamFileList` object created with BamFileList or a `BigWigFileList` object created with BigWigFileList.
`chrs` | The chromosome of the files to read. The format has to match the one used in the input files.
`bai` | The full path to the BAM index files. If `NULL` it is assumed that the BAM index files are in the same location as the BAM files and that they have the .bai extension. Ignored if `files` is a `BamFileList` object.
`chrlens` | The chromosome lengths in base pairs. If it's `NULL`, the chromosome length is extracted from the BAM files. Otherwise, it should have the same length as `chrs`.
`outputs` | This argument is passed to the `output` argument of loadCoverage. If `NULL` or `'auto'` it is then recycled.
`cutoff` | This argument is passed to filterData.
`...` | Arguments passed to other methods and/or advanced arguments. Advanced arguments: verbose If `TRUE` basic status updates will be printed along the way. mc.cores How many cores to use for reading the chromosome information. There's no benefit of using a number greater than the number of chromosomes. Also note that your harddisk speed will limit how much you get from using a higher `mc.cores` value. mc.cores.load Controls the number of cores to be used per chr for loading the data which is only useful in the scenario that you are loading in genome tiles. If not supplied, it uses `mc.cores` for loadCoverage. Default: 1. If you are using genome tiles, the total number of cores you'll use will be `mc.cores` times `mc.cores.load`. Passed to loadCoverage, define_cluster and extendedMapSeqlevels. Note that filterData is used internally by loadCoverage (and hence fullCoverage) and has the important arguments `totalMapped` and `targetSize` which can be used to normalize the coverage by library size. See getTotalMapped for calculating `totalMapped`.

### Value

A list with one element per chromosome.

Each element is a DataFrame with the coverage information produced by loadCoverage.

### Author(s)

Leonardo Collado-Torres

### See Also

loadCoverage, filterData, getTotalMapped

### Examples

    datadir <- system.file('extdata', 'genomeData', package='derfinder')
    files <- rawFiles(datadir=datadir, samplepatt='*accepted_hits.bam$',
        fileterm=NULL)
    ## Shorten the column names
    names(files) <- gsub('_accepted_hits.bam', '', names(files))

    ## Read and filter the data, only for 1 file
    fullCov <- fullCoverage(files=files[1], chrs=c('21', '22'))
    fullCov

    ## Not run:
    ## You can then use filterData() to filter the data if you want to.
    ## Use bplapply() if you want to do so with multiple cores as shown below.
    library('BiocParallel')
    p <- SnowParam(2L, outfile = Sys.getenv('SGE_STDERR_PATH'))
    bplapply(fullCov, function(x) {
        library('derfinder'); filterData(x, cutoff=0) }, BPPARAM = p)

    ## End(Not run)

[Package derfinder version 1.14.0 Index]