[BioC] Limma analyse factorial data with two-color arrays
Naomi Altman
naomi at stat.psu.edu
Wed May 27 14:38:04 CEST 2009
Limma codes for only one level of blocking. So, I guess you will
need to stick with 2 channel analysis with blocks to deal with
this. Fortunately, for a 2x2 design, things are still (pretty)
good. Assuming that M4=stim4-neg4 and M8=stim8-neg8 and A4 and A8
are defined with +.
The main effect of stim is the average of M4 and M8.
The interaction of stim with time is M8-M4.
But I do not think you can estimate the time main effect, which is
A8-A4 in this analysis. Note that A is not normalized.
--Naomi
At 07:51 AM 5/27/2009, Jabez Wilson wrote:
>Thanks for that suggestion, Naomi, I take it you mean "separate"
>channel analysis in the limma users guide. There's a complication,
>in that there are technical dye-swap replicates for each sample - I
>can't currently see how I can integrate this into the code. Could
>you perhaps suggest how and whether I can do this.
>TIA
>Jabez
>
>--- On Sat, 23/5/09, Naomi Altman <naomi at stat.psu.edu> wrote:
>
>
>From: Naomi Altman <naomi at stat.psu.edu>
>Subject: Re: [BioC] Limma analyse factorial data with two-color arrays
>To: "Jabez Wilson" <jabezwuk at yahoo.co.uk>, "James W. MacDonald"
><jmacdon at med.umich.edu>
>Cc: "bioconductor" <bioconductor at stat.math.ethz.ch>
>Date: Saturday, 23 May, 2009, 5:07 AM
>
>
>The simplest way to handle this type of design is to use the single
>channel analysis in limma. This allows you to use array as a block,
>and then do the treatment contrasts in the usual way.
>
>There is nothing wrong with the experimental design. It is an
>incomplete block design that allows the greatest precision for the
>stimulant effect within time. However, the main effect of time and
>the time by stimulant interaction can all be assessed.
>
>--Naomi
>
>At 10:24 AM 5/22/2009, Jabez Wilson wrote:
> >Thanks for you prompt reply, James. I'm familiar with you from your
> >postings on the SAM mailing lists, so am honoured that you can reply
> >to my query.
> >Whilst I don't want to take bread out of honest statisticians
> >mouths, I've been asked to analyse using limma an experiment that's
> >already taken place - the design was down to someone else and
> >obviously from your reply should have been done in a different way.
> >The experiment is quite simple in that spleen cells are incubated
> >with or without a stimulant (I think it's the ppd of the TB vaccine)
> >for either 4 or 8 hours, and the contrast of interest is whether
> >there is a difference in gene expression in the stimulated cells
> >between the two time periods.
> >I thought about analysing it using the "negs" as a common reference
> >as you suggest, but that would assume that there is no change in
> >expression in the unstimulated samples, which it not an assumption
> >that now can be proved one way or the other.
> >Up until now, I had been analysing them seperately, and just
> >comparing the lists of expressed genes, but thought that there could
> >be more information obtained by trying to "link" them. If it could
> >be done, then I could also answer the question of whether the
> >unstimulated "negs" are in fact different at the two time points.
> >I take it from your reply that this cannot actually be done now.
> >
> >Thanks again for replying,
> >
> >Jabez
> >--- On Fri, 22/5/09, James W. MacDonald <jmacdon at med.umich.edu> wrote:
> >
> >
> >From: James W. MacDonald <jmacdon at med.umich.edu>
> >Subject: Re: [BioC] Limma analyse factorial data with two-color arrays
> >To: "Jabez Wilson" <jabezwuk at yahoo.co.uk>
> >Cc: "bioconductor" <bioconductor at stat.math.ethz.ch>
> >Date: Friday, 22 May, 2009, 2:12 PM
> >
> >
> >Hi Jabez,
> >
> >Jabez Wilson wrote:
> > > Sorry.... sent too soon.
> > >
> > > Dear all, I know that this question has been asked in a couple of
> > > forms, but I haven't noticed a full reply given. I'm hoping that
> > > someone will be able to give me the exact answer. I'm comparing
> > > stimulated cells vs unstimulated cells on each slide at two time
> > > points (4 hrs and 8 hrs). Suppose there are 4 samples at each time
> > > point the targets file will look like this:
> > >
> > > FileName cy3 cy5 1 stim4 neg4 2 neg4
> > > stim4 3 stim4 neg4 4 neg4 stim4
> > >
> > > 5 stim8 neg8 6 neg8 stim8 7
> > > stim8 neg8 8 neg8 stim8
> > >
> > > There is no common reference (pool) as there is in the weaver example
> > > in the limma guide, so should I use e.g. neg4 as the reference i.e.
> > >
> > > design <- modelMatrix(targets,ref="neg4")
> > >
> > > If I do that then when I fit the model using lmFit(MA, design) I get
> > >
> > > "Coefficients not estimable: stim8 "
> >
> >You can't use neg4 as a reference when it isn't actually a reference
> >(e.g., it has to be on every slide). If you create a design matrix
> >this way you will get
> >
> > > modelMatrix(targets, ref="neg4")
> >Found unique target names:
> >neg4 neg8 stim4 stim8
> > neg8 stim4 stim8
> >[1,] 0 -1 0
> >[2,] 0 1 0
> >[3,] 0 -1 0
> >[4,] 0 1 0
> >[5,] 1 0 -1
> >[6,] -1 0 1
> >[7,] 1 0 -1
> >[8,] -1 0 1
> >
> >Which is not of full rank (e.g., the stim8 column is a linear
> >combination of the neg8 column).
> >
> >You don't give any information about your experiment, so it is
> >difficult to help. In addition, people are in general hesitant to
> >help people with experimental design or analysis questions because
> >a.) that is what many of us do for a living, so you are in effect
> >asking for pro bono work, and b.) without knowing more about a given
> >experiment it isn't reasonable for people to give analysis advice anyway.
> >
> >So, without knowing more about your experiment other than the short
> >names you gave your treatments, can you not simply analyze the '4'
> >samples separately from the '8' samples, using a reference design in
> >each case? Or if the negX samples are all supposed to be similar in
> >expression (and you can show they are), you could rename them 'neg'
> >and then have a true reference design.
> >
> >Best,
> >
> >Jim
> >
> >
> > >
> > > Can anyone help me from this point (apart from advising me to do the
> > > microarray expt with affymetrix chips)?
> > >
> > >
> > >
> > >
> > >
> > > [[alternative HTML version deleted]]
> > >
> > >
> > >
> > > ------------------------------------------------------------------------
> > >
> > >
> > > _______________________________________________ Bioconductor mailing
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> >
> >-- James W. MacDonald, M.S.
> >Biostatistician
> >Douglas Lab
> >University of Michigan
> >Department of Human Genetics
> >5912 Buhl
> >1241 E. Catherine St.
> >Ann Arbor MI 48109-5618
> >734-615-7826
> >
> >
> >
> >
> > [[alternative HTML version deleted]]
> >
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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
>
>
>
>
>
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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
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