segData
or segMeth
object. It does this by identifying
within replicate groups regions of the genome that satisfy the criteria
for being a locus and have no region within them that satisfies the
criteria for being a null. These criteria can be defined by the user or
inferred from the data.
heuristicSeg(sD, aD, gap = 100, RKPM = 1000, prop = 0.2, locCutoff = 0.99, subRegion =
NULL, largeness = 1e8, getLikes = TRUE, verbose = TRUE, tempDir = NULL,
cl = NULL, recoverFromTemp = FALSE)
alignmentData
or
methData
object.
segData
or segMeth
object derived from the `aD' object.
segData
object,
what RKPM (reads per kilobase per million reads) distinguishes
between a locus and a null region?segMeth
object,
what proportion of methylated cytosines distinguishes between a
locus and a null region?. Defaults to 0.2.segMeth
object, with what likelihood must the proportion of methylated
cytosines exceed the `prop' option? Defaults to 0.99.'data.frame'
object defining the subregions of
the genome to be segmented. If NULL (default), the whole genome is
segmented.lociData
object, containing count
information on all the segments discovered.
'cluster'
object (package: snow) may be used for
parallelisation of parts of this function when examining large data sets.
Passing NULL to this variable will cause the function to run in
non-parallel mode. If recoverFromTemp = TRUE
, the function will attempt to recover
a crashed position from the temporary files in tempDir
. At
present, the function assumes you know what you are doing, and will
perform no checking that these files are suitable for the specified
recovery. Use with caution.
classifySeg
, an alternative approach to this problem using an empirical
Bayes approach to classify segments.
plotGenome
, a function for plotting the alignment of
tags to the genome (together with the segments defined by this
function).
baySeq
, a package for discovering
differential expression in lociData
objects.
# Define the chromosome lengths for the genome of interest.
chrlens <- c(2e6, 1e6)
# Define the files containing sample information.
datadir <- system.file("extdata", package = "segmentSeq")
libfiles <- c("SL9.txt", "SL10.txt", "SL26.txt", "SL32.txt")
# Establish the library names and replicate structure.
libnames <- c("SL9", "SL10", "SL26", "SL32")
replicates <- c(1,1,2,2)
# Process the files to produce an `alignmentData' object.
alignData <- readGeneric(file = libfiles, dir = datadir, replicates =
replicates, libnames = libnames, chrs = c(">Chr1", ">Chr2"), chrlens =
chrlens)
# Process the alignmentData object to produce a `segData' object.
sD <- processAD(alignData, gap = 100, cl = NULL)
# Use the segData object to produce a segmentation of the genome.
segD <- heuristicSeg(sD = sD, aD = alignData,
subRegion = data.frame(chr = ">Chr1", start = 1, end = 1e5),
cl = NULL)
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