I have not used MAANOVA, but according to what I
have read, it will handle multiple random
effects, which in your case would be array and biological sample.
--Naomi
At 05:28 AM 9/18/2007, Muller, Pie wrote:
>Dear Naomi,
>
>I have attached a graphical representation of
>our experimental design in a jpg image to give
>you a quick idea. In our experiments the "targets" look as follows:
>
>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
>
>
>If limma cannot handle the above structure would
>"maanova" be a suitable package for the analysis of this type of
experiment?
>
>Best,
>Pie
>
>
>
>
>-----Original Message-----
>From: Naomi Altman [mailto:naomi at stat.psu.edu]
>Sent: 17 September 2007 20:44
>To: Muller, Pie; Naomi Altman; bioconductor at stat.math.ethz.ch
>Subject: Re: [BioC] Extremely low p-values in limma
>
>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 M?ller
> > >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
>
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