Description Usage Arguments Details Examples
This function evaluates the impact of different expression filters on the residuals of a fitted DESeqDataSet.
1 | check_resid(dds, filter = c(1, 1))
|
dds |
A |
filter |
Numeric vector of length two specifying the filter criterion. Each
probe must have at least |
The statistical tests upon which qmod
is based presume that residuals
for a fitted model are approximately normally distributed. This is not generally
true of negative binomal GLMs, the family of models used by DESeq. The
non-normality of residuals is especially pronounced for low count probes, which are
by default not filtered out until after modeling in the DESeq pipeline. (See
results
for more details.) To run qmod
on
DESeqDataSet
objects, it is necessary to filter out underexpressed probes
and apply a variance stabilizing transformation. We recommend applying the lightest
possible expression filter, although there is no precise algorithm for determining
what this should be.
As a rule of thumb, the limma
authors advise setting filter[1]
to 10
/ (L / 1,000,000), where L = the minimum library size for a given
count matrix; and setting filter[2]
to the number of replicates in the
largest group. These are broad guidelines, however, not strict rules.
1 2 3 4 | library(DESeq2)
dds <- makeExampleDEESeqDataSet()
dds <- DESeq(dds)
check_resid(dds)
|
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