Question: limma - FDR adjusted "p-values"
13.0 years ago by
Gordon Smyth ♦ 32k
Walter and Eliza Hall Institute of Medical Research, Melbourne, Australia
Gordon Smyth ♦ 32k wrote:
> Date: Mon, 31 Jan 2005 09:56:09 -0500 > From: Naomi Altman <firstname.lastname@example.org> > Subject: [BioC] limma - FDR adjusted "p-values" > To: email@example.com > > 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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