[BioC] split-plot factorial design with limma?

[Valeria Edefonti]

Hi all,
I'm analyzing an experiment using 10 Affymetrix chips.
I have 5 subjects, who are either healthy or having two different  
illnesses, TTD or CS.
Each subject is considered twice, at different levels of growth, P or  
D, of some of his cells.
Pairs are A and G, B and H and so on:

 >targets

     GROWTHLEVEL     ILLNESS
A	P	           			H
B	P	            			H
C	P	            			H
D	P	            			TTD
F	P	            			CS
G	D	           			H
H	D	            			H
I	D	            			H
L	D	            			TTD
N     D	            			CS

I adopted a factorial design with a blocking variable, blocco, for  
identifying the five subjects.
The code follows:

SS<-paste(targets$GROWTHLEVEL, targets$ILLNESS, sep=".")
SS<-factor(SS, levels=c("P.H","P.TTD","P.CS","D.H","D.TTD","D.CS"))

design.ch<-model.matrix(~0+SS)

colnames(design.ch)<-levels(SS)
rownames(design.ch)<-rownames(targets)

blocco<-c(1,2,3,4,5,1,2,3,4,5)

corfit<-duplicateCorrelation(MA, design=design.ch,block=blocco)

fit<-lmFit(MA, design.ch, block=blocco, cor=corfit$consensus)

contrast.matrix<-makeContrasts(H.PvsD=P.H-D.H, TTD.PvsD=P.TTD-D.TTD,  
CS.PvsD=P.CS-D.CS, P.TTDvsH=P.TTD-P.H, P.CSvsH=P.CS-P.H,  
D.TTDvsH=D.TTD-D.H, D.CSvsH=D.CS-D.H,levels=design.ch)

fit2<-contrasts.fit(fit, contrast.matrix)
fit3<-eBayes(fit2)

I guess so far so good, right?

However, a two treatment split-plot factorial design may be slightly  
better for my case, though I'm definitely not an expert in this field.
I see three groups of blocks:
Healthy: 3 blocks
TTD: TTD block
CS: CS block,
each one corresponding to a different level of factor "illness".
Is it a good idea?

Is there a way to perform such an analysis with limma?
If not, can I still stick to my current solution?
I know I can use lme() or aov(), but I should modify them to moderate  
the random effects across genes.

Thank you very much for your help.
Best wishes,
Valeria