DESeq, so is not
necessary to call on top of a DESeq call. See the minReplicatesForReplace
argument documented in link{DESeq}.replaceOutliers(object, trim = 0.2, cooksCutoff, minReplicates = 7,
whichSamples)replaceOutliersWithTrimmedMean(object, trim = 0.2, cooksCutoff,
minReplicates = 7, whichSamples)
assays(dds)[["cooks"]]. These are the Cook's distances which will
be used to define outlier counts.counts and the original counts preserved in
assays(dds)[["originalCounts"]]DESeq, nbinomWaldTest
or nbinomLRT, with values predicted by the trimmed mean
over all samples (and adjusted by size factor or normalization factor).
This function replaces the counts in the matrix returned by counts(dds)
and the Cook's distances in assays(dds)[["cooks"]]. Original counts are
preserved in assays(dds)[["originalCounts"]].The DESeq function calculates a diagnostic measure called
Cook's distance for every gene and every sample. The results
function then sets the p-values to NA for genes which contain
an outlying count as defined by a Cook's distance above a threshold.
With many degrees of freedom, i.e. many more samples than number of parameters to
be estimated-- it might be undesirable to remove entire genes from the analysis
just because their data include a single count outlier.
An alternate strategy is to replace the outlier counts
with the trimmed mean over all samples, adjusted by the size factor or normalization
factor for that sample. The following simple function performs this replacement
for the user, for samples which have at least minReplicates number
of replicates (including that sample).
For more information on Cook's distance, please see the two
sections of the vignette: 'Dealing with count outliers' and 'Count outlier detection'.
DESeq