Question: limma - FDR adjusted "p-values"

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13.1 years ago by

Gordon Smyth ♦

**33k**Walter and Eliza Hall Institute of Medical Research, Melbourne, Australia

Gordon Smyth ♦

**33k**wrote:> Date: Mon, 31 Jan 2005 09:56:09 -0500
> From: Naomi Altman <naomi@stat.psu.edu>
> Subject: [BioC] limma - FDR adjusted "p-values"
> To: bioconductor@stat.math.ethz.ch
>
> Just a suggestion:
>
> The FDR adjusted "p-values" are called "q-values" in much of the
> literature. I suggest that limma follow suit,
It's certainly true that a lot of users have trouble with FDR and with
adjusted p-values in
general. Perhaps you're right that limma should use the term
"q-values". This would associate
p-values with control/estimation of FWER and q-values with
control/estimation of FDR.
The reason I haven't this so far is because the term "q-value" coined
by John Storey seems to me
to measure something slightly different to Benjamini and Hocherg
adjusted p-values. I think that
John Storey's q-value uses a slightly different definition of false
discovery rate, namely pFDR,
the positive false rate. Also I think it usually estimates pFDR
rather than formally controlling
it. Although there is a value "Q" which appears in Benjamin and
Hochberg's formulations, and it
is closely related to q-values, it is not exactly the same. So I
have been reluctant to use the
term "q-value" for things which were not quite the same, as this would
cloud the fine meaning of
the term. Perhaps I am splitting hairs here and should just accept
the broad definition of
q-value for FDR or pFDR and p-value for FWER. Any other opinions?
I have also thought that perhaps topTable() should label the
p-value/q-value column in the output
to indicate which adjustment method was used to generate the table.
> and also add a line to the
> documentation (it might already be there and I missed it)
>
> "If the number of significant results at level alpha is less than
> alpha*(number of genes), then the q-value will be 1.0."
>
> It seems like I have to explain this to just about every
investigator who
> runs into this.
I get a lot of questions about this as well. Actually, the statement
you've made isn't always
true, although it usually is. Even if the smallest p-value out of n
genes is only as small as
1/n, the "fdr" adjusted p-value is not always 1. It can be as small
as 1/n depending on the other
n-1 p-values.
Perhaps the way to go would be for topTable() to output the raw
p-values as well as the adjusted
p-values/q-values. I haven't done this so as to keep the table as
small as possible, but it would
prevent users from being presented with just a list of p-values all
equal to 1. What do you
think?
Gordon
> Naomi S. Altman 814-865-3791 (voice)
> Associate Professor
> Bioinformatics Consulting Center
> Dept. of Statistics 814-863-7114 (fax)
> Penn State University 814-865-1348
(Statistics)
> University Park, PA 16802-2111

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modified 13.1 years ago
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Naomi Altman •

**6.0k**• written 13.1 years ago by Gordon Smyth ♦**33k**