Entering edit mode
lemerle@embl.de
▴
50
@lemerleemblde-1669
Last seen 10.3 years ago
dear list,
i am analysing a set of affymetrix chips using limma to get lists of
differentially expressed probesets.
concerning BH and BY, the two methods available with p.adjust that
adjust
p-values controlling for the false discovery rate, i was surprised to
find a
large discrepancy in the number of differentially expressed genes in
the case
of one contrast of interest:
> fit <- lmFit(eset.f,design)
> fit2 <- contrasts.fit(fit,c.matn)
> fit2 <- eBayes(fit2)
# adjustment for multiple testing of the set of p-values derived from
the first
contrast:
> p.value <- fit2$p.value[,1]
> bh <- p.adjust(p.value, method = "BH")
> by <- p.adjust(p.value, method = "BY")
# number of differentially expressed genes below the cutoff value of
0.01 for
the 2 methods
> length(which(bh<0.01))
[1] 1032
> length(which(by<0.01))
[1] 519
# size of the original set: 3549 probesets (with 15 observations for
each)
> dim(exprs(eset.f))
[1] 3549 15
i don't think there is a bug anywhere (but i'd gladly send you my data
to
reproduce this), i'm more inclined to think that i know too little to
make
sense of this at this stage....
could this difference be due to the fact that these numbers correspond
to a
large fraction of the whole set tested and that the two methods behave
differently with respect to this?
if estimates of false positives at this cutoff were reliable with both
methods,
wouldn't this imply that BH gives a far better coverage of the set of
truly
differentially expressed genes than BY (TP/(TP+FN)), for the same
control over
false discovery (here, 1%)?
i've seen mentioned but only vaguely that the magnitude of change is
an issue.
that the methods work best when it is low.... if you think that may be
it,
could you point me to something i might read to understand this
better?
a related question i have is about the interpretation of FDR values
close to 1,
in such cases where there are many changes, ie when the fraction of
non-differentially expressed genes in the whole set is far below 1...
i've come to understand that methods controlling fdr substitute the
p-value of
each gene for a value that corresponds to the minimum cut-off to apply
in order
for that gene to be called positive, with the added info that such a
set
obtained by applying this minimum cutoff can be expected to contain
that
percentage of false positives.
but as the fdr rate ranges from 0 to 1, the chosen cutoff can fall
above the
true percentage of non-differentially expressed sets and i don't see
how such
an estimate can still be reliable up to 1 if the true number of false
positives
is far lower.... unless it represents only an upper bound of how many
false
positives to expect... is this the case ?
thanks for any comment, i'm confused about this....
caroline
--
Caroline Lemerle
PhD student, lab of Luis Serrano
Structural Biology and Biocomputing Dept
EMBL, Heidelberg, Germany
tel 00-49-6221-387-8335
--
