[BioC] Yet another nested design in limma
Paolo Innocenti
paolo.innocenti at ebc.uu.se
Mon May 4 14:09:14 CEST 2009
Hi all,
since I received a few emails in my mailbox by people interested in a
solution for this design (or a design similar to this one), but there is
apparently no (easy) solution in limma, I was wondering if anyone could
suggest a package for differential expression analysis that allows
dealing with:
nested designs,
random effects,
multiple factorial designs with more than 2 levels.
I identified siggenes, maanova, factDesign that could fit my needs, but
I would like to have a comment by someone with more experience before
diving into a new package.
Best,
paolo
Paolo Innocenti wrote:
> Hi Naomi and list,
>
> some time ago I asked a question on how to model an experiment in limma.
> I think I need some additional help with it as the experiment grew in
> complexity. I also added a factor "batch" because the arrays were run in
> separate batches, and I think would be good to control for it.
> The dataframe with phenotypic informations ("dummy") looks like this:
>
> >> Phen Line Sex Batch BiolRep
> >> File1 H 1 M 1 1
> >> File2 H 1 M 1 2
> >> File3 H 1 M 2 3
> >> File4 H 1 M 2 4
> >> File5 H 1 F 1 1
> >> File6 H 1 F 1 2
> >> File7 H 1 F 2 3
> >> File8 H 1 F 2 4
> >> File9 H 2 M 1 1
> >> File10 H 2 M 1 2
> >> File11 H 2 M 2 3
> >> File12 H 2 M 2 4
> >> File13 H 2 F 1 1
> >> File14 H 2 F 1 2
> >> File15 H 2 F 2 3
> >> File16 H 2 F 2 4
> >> File17 L 3 M 1 1
> >> File18 L 3 M 1 2
> >> File19 L 3 M 2 3
> >> File20 L 3 M 2 4
> >> File21 L 3 F 1 1
> >> File22 L 3 F 1 2
> >> File23 L 3 F 2 3
> >> File24 L 3 F 2 4
> >> File25 L 4 M 1 1
> >> File26 L 4 M 1 2
> >> File27 L 4 M 2 3
> >> File28 L 4 M 2 4
> >> File29 L 4 F 1 1
> >> File30 L 4 F 1 2
> >> File31 L 4 F 2 3
> >> File32 L 4 F 2 4
> >> File33 A 5 M 1 1
> >> File34 A 5 M 1 2
> >> File35 A 5 M 2 3
> >> File36 A 5 M 2 4
> >> File37 A 5 F 1 1
> >> File38 A 5 F 1 2
> >> File39 A 5 F 2 3
> >> File40 A 5 F 2 4
> >> File41 A 6 M 1 1
> >> File42 A 6 M 1 2
> >> File43 A 6 M 2 3
> >> File44 A 6 M 2 4
> >> File45 A 6 F 1 1
> >> File46 A 6 F 1 2
> >> File47 A 6 F 2 3
> >> File48 A 6 F 2 4
>
> In total I have
> Factor "Phen", with 3 levels
> Factor "Line", nested in Phen, 6 levels
> Factor "Sex", 2 levels
> Factor "Batch", 2 levels
>
> I am interested in:
>
> 1) Effect of sex (M vs F)
> 2) Interaction between "Sex" and "Line" (or "Sex" and "Phen")
>
> Now, I can't really come up with a design matrix (not to mention the
> contrast matrix).
>
> Naomi Altman wrote:
>> You can design this in limma quite readily. Nesting really just means
>> that only a subset of the possible contrasts are of interest. Just
>> create the appropriate contrast matrix and you are all set.
>
> I am not really sure with what you mean here. Should I treat all the
> factors as in a factorial design?
> I might do something like this:
>
> phen <- factor(dummy$Phen)
> line <- factor(dummy$Line)
> sex <- factor(dummy$Sex)
> batch <- factor(dummy$Batch)
> fact <- factor(paste(sex,phen,line,sep="."))
> design <- model.matrix(~ 0 + fact + batch)
> colnames(design) <- c(levels(fact), "batch2")
> fit <- lmFit(dummy.eset,design)
> contrast <- makeContrasts(
> sex= (F.H.1 + F.H.2 + F.L.3 + F.L.4 + F.A.5 + F.A.6) - (M.H.1 +
> M.H.2 + M.L.3 + M.L.4 + M.A.5 + M.A.6),
> levels=design)
> fit2 <- contrasts.fit(fit,contrast)
> fit2 <- eBayes(fit2)
>
> In this way I can correctly (I presume) obtain the effect of sex, but
> how can I get the interaction term between sex and line?
> I presume there is a "easy" way, but I can't see it...
>
> Thanks,
> paolo
>
>
>>
>> --Naomi
>>
>> At 12:08 PM 2/16/2009, Paolo Innocenti wrote:
>>> Hi all,
>>>
>>> I have an experimental design for a Affy experiment that looks like
>>> this:
>>>
>>> Phen Line Sex Biol.Rep.
>>> File1 H 1 M 1
>>> File2 H 1 M 2
>>> File3 H 1 F 1
>>> File4 H 1 F 2
>>> File5 H 2 M 1
>>> File6 H 2 M 2
>>> File7 H 2 F 1
>>> File8 H 2 F 2
>>> File9 L 3 M 1
>>> File10 L 3 M 2
>>> File11 L 3 F 1
>>> File12 L 3 F 2
>>> File13 L 4 M 1
>>> File14 L 4 M 2
>>> File15 L 4 F 1
>>> File16 L 4 F 2
>>>
>>>
>>> This appears to be a slightly more complicated situation than the one
>>> proposed in the section 8.7 of the limma users guide (p.45) or by
>>> Jenny on this post:
>>>
>>> https://stat.ethz.ch/pipermail/bioconductor/2006-February/011965.html
>>>
>>> In particular, I am intersted in
>>> - Effect of "sex" (M vs F)
>>> - Interaction between "sex" and "phenotype ("line" nested)
>>> - Effect of "phenotype" in males
>>> - Effect of "phenotype" in females
>>>
>>> Line should be nested in phenotype, because they are random "strains"
>>> that happened to end up in phenotype H or L.
>>>
>>> Can I design this in limma? Is there a source of information about
>>> how to handle with this? In particular, can I design a single model
>>> matrix and then choose the contrasts I am interested in?
>>>
>>> Any help is much appreciated,
>>> paolo
>>>
>>>
>>> --
>>> Paolo Innocenti
>>> Department of Animal Ecology, EBC
>>> Uppsala University
>>> Norbyvägen 18D
>>> 75236 Uppsala, Sweden
>>>
>>> _______________________________________________
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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
>>
>>
>
--
Paolo Innocenti
Department of Animal Ecology, EBC
Uppsala University
Norbyvägen 18D
75236 Uppsala, Sweden
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