Description Usage Arguments Details Value Author(s) See Also Examples
View source: R/preprocessCoverage.R
This function takes the coverage data from loadCoverage, scales the data, does the log2 transformation, and splits it into appropriate chunks for using calculateStats.
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preprocessCoverage( coverageInfo, groupInfo = NULL, cutoff = 5, colsubset = NULL, lowMemDir = NULL, ... )
A list containing a DataFrame –
A factor specifying the group membership of each sample. If
The base-pair level cutoff to use. It's behavior is controlled
Optional vector of column indices of
If specified, each chunk is saved into a separate Rdata
Arguments passed to other methods and/or advanced arguments. Advanced arguments:
mc.cores is used to
chunksize. This is useful if you want to split the data
so each core gets the same amount of data (up to rounding).
Computing the indexes and using those for
memory copying as described by Ryan Thompson and illustrated in approach #4
lowMemDir is specified then
$coverageProcessed is NULL and
$mclapplyIndex is a vector with the chunk identifiers.
A list with five components.
contains the processed coverage information in a
DataFrame object. Each column represents a sample and the coverage
information is scaled and log2 transformed. Note that if
NULL the number of columns will be less than those in
coverageInfo$coverage. The total number of rows depends on the number
of base pairs that passed the
cutoff and the information stored is
the coverage at that given base. Further note that filterData is
colsubset is not
NULL and could thus lead to
fewer rows compared to
is a list of logical Rle objects. They contain the
partioning information according to
is a logical Rle with the positions of the chromosome that passed the cutoff.
is a numeric Rle with the mean coverage at each filtered base.
is a list of Rle objects containing the mean coverage at
each filtered base calculated by group. This list has length 0 if
Passed to filterData when
colsubset is specified.
filterData, loadCoverage, calculateStats
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## Split the data and transform appropriately before using calculateStats() dataReady <- preprocessCoverage(genomeData, cutoff = 0, scalefac = 32, chunksize = 1e3, colsubset = NULL, verbose = TRUE ) names(dataReady) dataReady
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