Hi Ryan
On 21/03/13 01:05, Ryan C. Thompson wrote:
> So, we have several great packages for testing for evidence of
> differential expression using RNA-seq counts, and I am happily using
> those. However, I would like to know if there is any method for
testing
> for evidence of equivalent expression. That is, I would like to show
> with some level of confidence that treatment X has no (or
negligible)
> effect on gene Y. Is there any way to conduct such a test?
One of the new features of DESeq2 is that it provides a empirical-
Bayes
style shrinkage estimates of coefficients (i.e., log fold changes) and
also estimates standard errors for these coefficients (taken from the
the reciprocal curvature of the posterior). Your task is one of the
application we had in mind for this.
You want to know whether the fold change is zero or nearly zero, and
you
also want to be ascertained that this estimate of a nearly-zero fold
change is a precise one. So, to find genes whose absolute log fold
change is _reliably_ smaller than some threshold, take all those genes
for which the estimated abs log fold change is below the threshold and
the standard error is well below the threshold, too.
The reason, why I write "below a threshold" rather than "equal to
zero"
is that it's biologically unreasonable to assume that a treatment has
exactly zero effect on a gene's expression. Gene regulation is such an
interconnected network that, at least in my view, every gene will
react
ever so slightly to every perturbance, but often, this reaction is too
small to be measurable. This is why you should define a threshold for
"biologically unlikely to be relevant". Let's say, we don't believe
that
an expression change of less than 15% (0.2 on a log2 scale) is worth
bothering. So, if you might take all genes with abs log2 fold change
below 0.2, and furthermore require that the standard error of this
log2
fold change estimate is below, say, 0.05, than you will get gene with
true values well below at least 0.3 or so.
In the end, the thresholds won't matter too much but if you want real
p
values, this is possible, too: Simply divide the log2 fold change
estimates by their standard errors to get z scores (this works because
the sampling distribution of our fold change estimates is reasonably
close to normal), and do two one-sided tests (TOST) as suggested by
Ryan
above.
Actually, as this may come up more often, we should maybe explain the
details of using this approach in the vignette. Ask again if you want
to
see some code.
Simon
