fullCoverage | R Documentation |
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.
fullCoverage(
files,
chrs,
bai = NULL,
chrlens = NULL,
outputs = NULL,
cutoff = NULL,
...
)
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 |
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 |
chrlens |
The chromosome lengths in base pairs. If it's |
outputs |
This argument is passed to the |
cutoff |
This argument is passed to filterData. |
... |
Arguments passed to other methods and/or advanced arguments. Advanced arguments:
Passed to loadCoverage, define_cluster and
extendedMapSeqlevels.
Note that filterData is used internally
by loadCoverage (and hence fullCoverage) and has the important
arguments |
A list with one element per chromosome.
Each element is a DataFrame with the coverage information produced by loadCoverage.
Leonardo Collado-Torres
loadCoverage, filterData, getTotalMapped
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)
bplapply(fullCov, function(x) {
library("derfinder")
filterData(x, cutoff = 0)
}, BPPARAM = p)
## End(Not run)
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