[BioC] Limma model parameters design, combining a paired with a factorial design

[James W. MacDonald]

Hi John,

On 10/13/2011 1:29 PM, john herbert wrote:
> Hello,
> I have a complex design matrix to produce (for me anyway) for single
> colour array data.
>
> I have 16 samples for 8 patients, which are paired (P1 to P4).
>
> Patients 1 and 3 are control patients and patients 2 and 4 are affected
>
> Before and after refer to before and after an operation.
>
> File1 P1 Control before
> File2 P1 Control after
> File3 P2 Affected before
> File4 P2 Affected after
> File5 P3 Control before
> File6 P3 Control after
> File7 P4 Affected before
> File8 P4 Affected after
>
> If to look at the Limma manual, section "8.3 Paired Samples" looks
> almost good for this design.
>
> However, does it need combining with a factorial design (section "8.7
> Factorial Designs") as there is a before and after op?

You don't have a factorial design. This is just a simple control vs 
affected with pairing.

If the before and after were different people, this could be analyzed as 
a factorial design, but since they are the same person you cannot assume 
independence between the before and after samples.

pairs <- factor(rep(1:4, each = 2))
type <- factor(rep(1:2, each = 2, times = 2))

design <- model.matrix(~type+pairs)
fit <- lmFit(eset, design)
fit2 <- eBayes(fit)

topTable(fit2, coef = 2)

Best,

Jim



>
> How do I combine a design for paired samples with a factorial design?
>
>
> > From the manual, below, the paired and the factorial examples looks
> scary to combine. ?
>
> FileName SibShip Treatment
> File1 1 C
> File2 1 T
> File3 2 C
> File4 2 T
> File5 3 C
> File6 3 T
>
> A moderated paired t-test can be computed by allowing for sib-pair
> eects in the linear model:
>> SibShip<- factor(targets$SibShip)
>> Treat<- factor(targets$Treatment, levels=c("C","T"))
>> design<- model.matrix(~SibShip+Treat)
>> fit<- lmFit(eset, design)
>> fit<- eBayes(fit)
>> topTable(fit, coef="TreatT")
>
> frame:
>
> FileName Strain Treatment
> File1 WT U
> File2 WT S
> File3 Mu U
> File4 Mu S
> File5 Mu S
>
>> TS<- paste(targets$Strain, targets$Treatment, sep=".")
>> TS
> [1] "WT.U" "WT.S" "Mu.U" "Mu.S" "Mu.S"
>
>> TS<- factor(TS, levels=c("WT.U","WT.S","Mu.U","Mu.S"))
>> design<- model.matrix(~0+TS)
>> colnames(design)<- levels(TS)
>> fit<- lmFit(eset, design)
>> cont.matrix<- makeContrasts(
>    WT.SvsU=WT.S-WT.U,
>    Mu.SvsU=Mu.S-Mu.U,
>    Diff=(Mu.S-Mu.U)-(WT.S-WT.U), levels=design)
>> fit2<- contrasts.fit(fit, cont.matrix)
>> fit2<- eBayes(fit2)
> We can use topTable() to look at lists of dierentially expressed
> genes for each of three
> contrasts, or else
> 48>  results<- decideTests(fit2)
>> vennDiagram(results)
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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

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