Hello, 
I use model.matrix () to create my contrast to do a canonical analysis  with rdatest() and I would like your opinion on my problem.

DATA:
I work with a microarray experiment composed 12 chips, 2 factors ( mutation , treatement) , each has 2 levels (absence , presence) , 3 biological replicat.

DESIGN:
no-mut1
no-mut2
no-mut3
mut1
mut2
mut3
TREATED_no-mut1
TREATED_no-mut2
TREATED_no-mut3
TREATED_mut1
TREATED_mut2
TREATED_mut3

CODE IN R:

facteur_mutation = gl(2,3, length = 12)
facteur_oxidant = gl(2,6)
helmert = model.matrix (~facteur_mutation*facteur_oxidant, contrast = list(facteur_mutation ="contr.helmert", facteur_oxidant= "contr.helmert")) 

MY RESULT ISSUE FROM CANONICAL ANALYSIS:
63 % of variance explained by mutation 
17% of variance explained by Treatement.

QUESTION: 
Given that 20 % of variance is no explained , how to integrage ( in R code ) the factor " replication "

I have tried to add in my original code this part :

the line factor_replication = c(1,2,3,1,2,3,1,2,3,1,2,3)
helmert = model.matrix (~facteur_mutation*facteur_oxidant* facteur_replication , contrast = list(facteur_mutation ="contr.helmert", facteur_oxidant= "contr.helmert", facteur_replication= "contr.helmert")) 

but I don't think that it's OK, because ( if I have good comprehension of what is helmert is ), the scalar product is no 0 ) . Moreover , the result "said"  that the variance explained by replication is 2 %... It's very low , too low...

What Does you think about integrate replication factor in my model.matrix ?

Thanks

Greg
Centre de recherche du CHUM. Montreal 


      
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