LIMMA P-value calculations/Suggestions for flagged data
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@gordon-smyth
Last seen 19 minutes ago
WEHI, Melbourne, Australia
> Date: Wed, 21 Mar 2007 16:04:31 -0400 > From: "Lance E. Palmer" <lance.palmer at="" stonybrook.edu=""> > Subject: [BioC] LIMMA P-value calculations/Suggestions for flagged > data > To: bioconductor at stat.math.ethz.ch > > I just had a question/concern about P value calculations in Limma (I am > using latest version of Bioconductor) > > I recently ran 3 arrays through my analysis. The slides were analayzed > with Genepix software. There were a couple of genes that concerned me. > One had a log fold change of -3.765. The adjusted p-value (fdr) > was .027. I looked at the individual M values for the arrays and they > were -0.009336, 0.09217 and -3.765. > > I noticed that the first two arrays had a 'not found' flag. So > basically the analysis gave a significant P-value using only 1 piece of > data. Is this something that is correct? Yes, it is correct. If there is only one data value with weight>0 for a particular probe, then limma uses the empirical Bayes prior standard deviation for that probe to form a t-statistic. Think of it this way. You observed M=-3.765 for this probe. That's a large negative value. You know from looking at the other probes that the standard deviation of M-values is usually around 0.03, say, so -3.7 is very likely genuinely different from zero. > I also wonder if I should even remove 'not found' flagged data. > Originally I did not, but someone suggested I do. I originally did not > remove it because of the case listed above. I've argued on this mailing list and elsewhere for a long time that, rather than flagging faint spots, it's better to use a better background correction method that avoids a blow out of M-values at low intensities. Best wishes Gordon > However, the case above tells us something about the experiments. How > do people deal with this situation? > > -Lance Palmer
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Entering edit mode
@gordon-smyth
Last seen 19 minutes ago
WEHI, Melbourne, Australia
Dear Sergio, Yes, RMA and gcRMA avoid a blowout of variability at low intensities. In fact they are very aggressive algorithms in this regard. Note: you still might filter low intensities probes in some circumstances, but you need to remove entire probes, entire data rows, not individual data values. Filtering individual spots or individual cells on intensity doesn't make sense to me. Best wishes Gordon > Date: Fri, 23 Mar 2007 18:40:59 -0800 (PST) > From: "Sergio Barberan" <barberan at="" biology.ucsc.edu=""> > Subject: Re: [BioC] LIMMA P-value calculations/Suggestions for flagged > data > To: bioconductor at stat.math.ethz.ch > >> I've argued on this mailing list and elsewhere for a long time that, >> rather than flagging faint >> spots, it's better to use a better background correction method that >> avoids a blow out of M-values >> at low intensities. > > Is RMA such a good background correction method? > > cheers, > sergio
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