[BioC] Extremely low p-values in limma

[Muller, Pie]

Naomi,

Thank you very much for your reply, the p-values seem to make much more sense now although I am still slightly confused. I tried to follow Example 8.2 of the Limma User's Guide by taking into account that RNA from each individual (e.g., "A1") appeared on four arrays. Why would my previous experimental design not follow the same logic as in example 8.2?

Apologies for coming back on this...

Thanks,
Pie

-----Original Message-----
From: Naomi Altman [mailto:naomi at stat.psu.edu] 
Sent: 17 September 2007 14:32
To: Muller, Pie; bioconductor at stat.math.ethz.ch
Subject: Re: [BioC] Extremely low p-values in limma

Yes, your code is treating the technical 
replicates as if they were the biological 
replicates and the biological replicates as if 
they were different treatments.  This is because 
A1 and A2 are each given a factor.  You need to 
rename all of the A's with the name "A", similarly for the Bs and Cs.

--Naomi

At 06:13 AM 9/17/2007, Muller, Pie wrote:
>Dear all
>
>I am analysing data obtained from an experiment 
>with an interwoven loop design using limma. The 
>design and the code are listed below. Many of 
>our probes show extremely low adjusted p-values 
>with values low as 1.748434e-71. Hence, I was 
>wondering whether my code somehow treats 
>technical replication as independent ones, or 
>whether such low p-values could be genuine. Has anyone any ideas?
>
>Many thanks for your suggestions!
>
>Pie
>
>
>My experimental design:
>
>We have 3 groups, A, B and C with 5 biological 
>(independent) replicates for each group (15 RNA 
>targets in total). The RNA's were co-hybridised 
>to a two colour array whereby each target was 
>twice labelled with Cy3 and twice with Cy5 in the following way:
>
>File            Cy3     Cy5
>
>File1           A1      C2
>File2           A1      B1
>File3           A2      C3
>File4           A2      B2
>File5           A3      C4
>File6           A3      B3
>File7           A4      C5
>File8           A4      B4
>File9           A5      C1
>File10  A5      B5
>File11  B1      A3
>File12  B1      C1
>File13  B2      C2
>File14  B2      A4
>File15  B3      C3
>File16  B3      A5
>File17  B4      C4
>File18  B4      A1
>File19  B5      C5
>File20  B5      A2
>File21  C1      A2
>File22  C1      B3
>File23  C2      A3
>File24  C2      B4
>File25  C3      A4
>File26  C3      B5
>File27  C4      A5
>File28  C4      B1
>File29  C5      A1
>File30  C5      B2
>
>
>My code for fitting the linear model:
>
>design=modelMatrix(targets, ref="A1")
>cor=duplicateCorrelation(MA, design, ndups=4, spacing=1, weights=w)
>fit=lmFit(MA, cor=cor$consensus.correlation, 
>design, ndups=4, spacing=1, weights=w)
>cont.matrix=makeContrasts(AvsB=(A2+A3+A4+A5-B1-B2-B3-B4-B5)/5, 
>AvsC=(A2+A3+A4+A5-C1-C2-C3-C4-C5)/5, 
>CvsB=(C1+C2+C3+C4+C5-B1-B2-B3-B4-B5)/5, levels=design)
>fit2=contrasts.fit(fit, cont.matrix)
>fit2=eBayes(fit2)
>topTable(fit2, coef="AvsB", adjust.method="fdr", sort.by="p")
>
>
>-------------------------------------
>
>Dr Pie Müller
>Vector Group
>Liverpool School of Tropical Medicine
>Pembroke Place
>Liverpool
>L3 5QA
>UK
>
>Tel +44(0) 151 705 3225
>Fax +44(0) 151 705 3369
>
>http://www.liv.ac.uk/lstm
>http://www.ivcc.com
>
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Naomi S. Altman                                814-865-3791 (voice)
Associate Professor
Dept. of Statistics                              814-863-7114 (fax)
Penn State University                         814-865-1348 (Statistics)
University Park, PA 16802-2111