preprocessQuantile(object, fixOutliers = TRUE, removeBadSamples = FALSE, badSampleCutoff = 10.5, quantileNormalize = TRUE, stratified = TRUE, mergeManifest = FALSE, sex = NULL, verbose = TRUE)RGChannelSet or
[Genomic]MethylSet.GenomicRatioSet
This function implements stratified quantile normalization preprocessing
for Illumina methylation microarrays. If removeBadSamples is
TRUE we calculate the median Meth and median Unmeth signal for
each sample, and remove those samples where their average falls below
badSampleCutoff. The normalization procedure is applied to the
Meth and Unmeth intensities separately. The distribution of type I and
type II signals is forced to be the same by first quantile normalizing
the type II probes across samples and then interpolating a reference
distribution to which we normalize the type I probes. Since probe types
and probe regions are confounded and we know that DNAm distributions
vary across regions we stratify the probes by region before applying
this interpolation. For the probes on the X and Y chromosomes we
normalize males and females separately using the gender information
provided in the sex argument. If gender is unspecified
(NULL), a guess is made using by the getSex function using
copy number information. Background correction is not used, but very
small intensities close to zero are thresholded using the
fixMethOutlier. Note that this algorithm relies on the
assumptions necessary for quantile normalization to be applicable and
thus is not recommended for cases where global changes are expected such
as in cancer-normal comparisons.
Note that this normalization procedure is essentially similar to one previously presented (Touleimat and Tost, 2012), but has been independently re-implemented due to the present lack of a released, supported version.
getSex, minfiQC,
fixMethOutliers for functions used as part of
preprocessQuantile.
## Not run:
# if(require(minfiData)) {
# GMset <- preprocessQuantile(RGsetEx)
# }
# ## End(Not run)
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