[BioC] Yet another nested design in limma
Paolo Innocenti
paolo.innocenti at ebc.uu.se
Mon Apr 27 17:00:45 CEST 2009
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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