Entering edit mode
At 05:48 AM 7/08/2004, Richard Friedman wrote:
>Gordon,
>
> I now believe that I understand your answer. In order to do
> adjust for both multiple comparisons and
>multiple tests I use classifyTestsF() with method="fdr". If I
understand
>the documentation correctly, the fdr part
>refers to the multiple test correction across genes on top of the
multiple
>comparison adjustment across levels performed
> if no method were to be specified.
>
> Do I have it straight?
No, not quite. classifyTestsF() doesn't actually have a 'method'
option, so
you can't use it with method="fdr".
Please understand that making multiple adjustments simutaneously
across
genes and across contrasts (comparisons between levels for example) is
still an unsolved problem, so it may be too much to expect neat
solutions
from limma or concise explanations from me.
One way that you could proceed is to compute an F-test p-value for
each
gene, testing for any differences betwen the levels of your factor,
and use
p.adjust() to apply "fdr" adjustment to those p-values across genes.
Then
for those genes which are chosen as showing some differences, you can
examine the individual contrast p-values more closely to decide which
levels are different.
Notice that the F-test p-value is computed automatically by eBayes()
and is
stored as fit$F.p.value in your MArrayLM linear model data object.
Here is some example code which applies "fdr" adjustment in a
hierarchical
fashion, first at the overall gene level, and then at the contrast
level
after deciding on the gene cutoff:
fit <- eBayes(fit)
selectedgenes <- p.adjust(fit$F.p.value, method="fdr") < 0.05
pmax <- pmin(fit$F.p.value[!selectedgenes], na.rm=TRUE)
results <- classifyTestsP(fit, p.value=pmax, method="fdr")
Gordon
>Thanks and best wishes,
>Rich
>On Aug 6, 2004, at 9:57 AM, Gordon Smyth wrote:
>
>>At 11:36 PM 6/08/2004, Richard Friedman wrote:
>>>Gordon,
>>>
>>> Thank you for answering my questions. The last equation in
your
>>> paper makes intuitive sense to me.
>>>
>>> I'm wondering if you can take the time to answer two more
>>> questions:
>>>
>>>1. Say I have the following case:
>>>
>>>Level A (3 replicates)
>>>Level B (2 replicates)
>>>Level C(1 replicate)
>>>Level D(1 replicate)
>>>
>>>Can I legitimately calculate a P value for the contrast Level A to
level
>>>C in the linear model even though I have only one replicate on
Level C.
>>>I am not talking about just Limma here. I am talking about the
linear
>>>model in general.
>>
>>Given assumption of common variance across levels, yes.
>>
>>> Also,
>>>I realize that one replicate is poor experimental design. This is
what I
>>>was given to analyze.
>>>
>>>2. If I wished to apply a multiple test correction to the pvalues
from
>>>non-orthogonal contrasts, would the following procedure be
legitimate::
>>>
>>> 1. Generate a pvalue for each contrast in the set of
>>> nonothogonal contrasts for each gene using classifyTestsF().
>>> 2. Correct the pvalues using a multiple test correction
such as
>>> FDR.
>>
>>Nothing special about this design. All usual things, e.g. in limma,
apply.
>>
>>Gordon
>>
>>>I realize that no multiple-test correction is entirely
satisfactory, I
>>>just want to get an approximate estimate of the p-values for each
>>>contrast as a guide to further experimentation and literature
searching.
>>>
>>>Best wishes,
>>>Rich
