rpMatrix: Reads per million profile matrices for plotting
In recoup: An R package for the creation of complex genomic profile plots

Description Usage Arguments Details Value Author(s) Examples

This function fills the profile field in the main input argument in recoup function by calculating profile matrices using reads per million (rpm) or reads per kb per million reads (rpkm) over binned genomic areas of interest, instead of genomic coverage signals. The profile matrices are used for later plotting.

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    rpMatrix(input, mainRanges, flank, binParams,
        strandedParams = list(strand = NULL, ignoreStrand = TRUE),
        rc = NULL)

`input`	an input list as in `recoup` but with the `ranges` field of each member filled (e.g. after using `preprocessRanges`.
`mainRanges`	the `genome` from `recoup` as a `GRanges` object (e.g. the output from `makeGRangesFromDataFrame`).
`flank`	see the `flank` argument in the main `recoup` function.
`binParams`	see the `binParams` argument in the main `recoup` function.
`strandedParams`	see the `strandedParams` argument in the main `recoup` function.
`rc`	fraction (0-1) of cores to use in a multicore system. It defaults to `NULL` (no parallelization).

Regarding the calculation of rpm and rpkm values, the calculations slightly differ from the default defintions of these measurements in the sense that they are also corrected for the bin lengths so as to acquire human-friendly values for plotting.

Note that the genomic ranges (BAM/BED files) must be imported before using this function (as per the default recoup pipeline). We plan to support streaming directly from BAM files in the future.

Same as input with the profile fields filled.

Panagiotis Moulos

# Load some data
data("recoup_test_data",package="recoup")
# Do some work
testGenomeRanges <- makeGRangesFromDataFrame(df=test.genome,
    keep.extra.columns=TRUE)
w <- width(testGenomeRanges)
testGenomeRanges <- promoters(testGenomeRanges,upstream=2000,downstream=0)
testGenomeRanges <- resize(testGenomeRanges,width=w+4000)
test.input <- rpMatrix(
    test.input, 
    mainRanges=testGenomeRanges,
    flank=c(2000,2000),
    binParams=list(flankBinSize=50,regionBinSize=150,binType="fixed",
        sumStat="mean",interpolation="spline"),
    rc=0.1
)