[BioC] Extremely low p-values in limma

[Naomi Altman]

Pie,

I do not recall all the examples but you have:

2 color arrays - hence correlation on the same array
4 technical reps - hence correlation on the same biological replicates

I did not check your code before, but I think 
that you need to show what is in "targets" for me to help you more.

I think that to handle this analysis, you need to 
use single channel analysis.  But then you have 2 
sources of dependence, and limma cannot handle this.

--Naomi

At 10:16 AM 9/17/2007, Muller, Pie wrote:
>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
> >
> >_______________________________________________
> >Bioconductor mailing list
> >Bioconductor at stat.math.ethz.ch
> >https://stat.ethz.ch/mailman/listinfo/bioconductor
> >Search the archives:
> >http://news.gmane.org/gmane.science.biology.informatics.conductor
>
>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
>
>_______________________________________________
>Bioconductor mailing list
>Bioconductor at stat.math.ethz.ch
>https://stat.ethz.ch/mailman/listinfo/bioconductor
>Search the archives: 
>http://news.gmane.org/gmane.science.biology.informatics.conductor

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