[BioC] Limma double paired analysis

[john seers (IFR)]

Hello All

I am having some difficulty in trying to use limma for a paired
analysis. Can anybody suggest an approach that would work?

The arrays are Affymetrix.

The experiment arrays look like:

Volunteer1              Ctrl <---------->Placebo<-------Time
passes------>Ctrl<------------>Drug
Volunteer2              Ctrl <---------->Placebo<-------Time
passes------>Ctrl<------------>Drug
Volunteer3              Ctrl <---------->Drug<----------Time
passes------>Ctrl<------------>Placebo
...

That is a control sample is taken. A treatment is given - Placebo or
Drug. Another sample is taken. A suitable time period passes and this is
repeated but with the Placebo or Drug treatments reversed.

How can this be analysed?

Looking at the Limma User Guide 8.3 Paired Samples looks to be a good
start. But is it possible to do some form of double pairing analysis?
That is I can pair the Ctrl arrays with their paired treatment. But then
I want to pair the Placebo with the Drug. How can I do this using limma?


========================================================================
=============

So far I have something like the following, if it helps. How can I
factor in that Drug Treatment and the Placebo treatment are paired? 


Treatment<-c("Drug", "Placebo", "ACtrl", "ACtrl", "ACtrl", "Drug",
"ACtrl",
    "Placebo", "ACtrl", "Placebo", "ACtrl", "Drug", "Placebo", "ACtrl",
"ACtrl",
    "Drug", "Drug", "ACtrl", "ACtrl", "Placebo", "Placebo", "ACtrl",   
    "Drug", "ACtrl", "Drug", "Placebo", "ACtrl", "ACtrl", "ACtrl",
"Placebo",
    "ACtrl", "Drug", "ACtrl", "Placebo", "Drug", "ACtrl", "Placebo",
"ACtrl",
    "ACtrl", "Drug", "Placebo", "ACtrl", "Drug", "ACtrl", "ACtrl",
"ACtrl",    
    "Drug", "Placebo") 


Pairing<-c("A13", "B13", "B13", "A13", "A17", "B17", "B17", "A17", "B8",
"A8",  "A8",
    "B8",  "B18", "B18", "A18", "A18", "A6",  "B6",  "A6",  "B6",
"A33", "B33",
    "B33", "A33", "A27", "B27", "A27", "B27", "A22", "B22", "B22",
"A22", "B11", "A11",
    "B11", "A11", "A28", "A28", "B28", "B28", "A16", "A16", "B16",
"B16", "A20", "B20", "A20", "B20")
   

Pairing<-factor(Pairing)
Treatment<-factor(Treatment)
design<-model.matrix(~ 0 + Pairing + Treatment)
fit<-lmFit(eset, design)
contrast.matrix<-makeContrasts(AbovePlacebo = TreatmentDrug -
TreatmentPlacebo, levels=design)
fit2<-contrasts.fit(fit, contrast.matrix)
eb<-eBayes(fit2)
tt<-topTable(eb)

Any help gratefully received.


Regards and thanks


John Seers




---

John Seers
Bioinformatics & Statistics
Institute of Food Research
Norwich Research Park
Colney
Norwich
NR4 7UA

Location: IFR1 N102
PC Machine ID: N198


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e-mail john.seers at bbsrc.ac.uk                        
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