I am facing a similar problem, and here is what I plan. (I am
putting out this suggestion for general discussion).
Step 1: Use duplicateCorrelation in limma. The genes with 6 spots
will be treated as 2 groups of 3. We need equal groups for the eBayes
Step 2: Adjust the eBayes estimate of variance for 6 spots instead of
3. Compute all the contrasts using all 6 spots, and write a bit of
code to redo the tests on the 6 spots data.
I think this is better than using the extra spots to check the
consistency of results, as has been suggested previously on this
list. We have more spots for some genes because, given the space on
the array, we did extra duplication of genes that were of primary
interest. We ought to use the increased power this gives us.
p.s. In our case, we have either 1 or 2 spots per gene. In designing
an array, I probably would use 4 spots for every gene rather than 3
for some and 6 for others.
At 11:03 AM 11/9/2005, alex lam (RI) wrote:
>Thanks for your reply. The probes are identical and every gene is
>replicated but not in the same number. Some are replicated 3 times
>and some 6 times. Is that going to be a problem?
>I should rephrase the comment on the spots with zero weights. I knew
>that they were ignored in normalization. Are they also ignored in
>other limma methods? I had to explicitly exclude them in boxplot,
>but I guess boxplot is just a generic method.
>Department of Genetics and Genomics
>Roslin Institute (Edinburgh)
>Midlothian EH25 9PS
>Phone +44 131 5274471
>From: Gordon Smyth [mailto:smyth at wehi.edu.au]
>Sent: 08 November 2005 23:17
>To: alex lam (RI)
>Cc: BioC Mailing List
>Subject: [BioC] averaging replicates within arrays
>Do you have the same number of replicate spots for every gene of
>and are the replicate probes identical? If so, see the case study in
>User's Guide on "Within array replicate spots".
>If only some of your genes are replicated, or if the probes are not
>identical, I would strongly advice you not to attempt to pre-
>average the spots. There is little to be gained and much to be lost.
>I don't understand you comment about ignoring spots with zero weight.
>already does this.
> >[BioC] averaging replicates within arrays
> >alex lam (RI) alex.lam at bbsrc.ac.uk
> >Tue Nov 8 23:49:46 CET 2005
> >Dear Colleagues,
> >Hi, I am a first year PhD student recently started on a project
> >microarray data analysis at the Roslin Institute in Scotland. I
> >managed to follow the limma vignette in loading the data and
> >the default normalization within arrays. On each array, probes of
> >genes have been placed in more than one spot. What I would like is
> >is to group spots by gene names in MA$genes and calculate the
> >logratio as the expression level (better still, ignore the spots
> >I guess I can dump the data and process it in perl but would like
> >how to do this a bit more elegantly in R. Your help is greatly
> >Many thanks,
>Bioconductor mailing list
>Bioconductor at stat.math.ethz.ch
Naomi S. Altman 814-865-3791 (voice)
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Penn State University 814-865-1348
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