Hello,
I have a matrix
> dim(dat)
[1] 15568 132
It looks like this:
NoD_14381_norm.1 NoD_14381_norm.2 NoD_14381_norm.3
NoD_14520_30mM.1 NoD_14520_30mM.2 NoD_14520_30mM.3
Ku8QhfS0n_hIOABXuE 4.75 4.25 4.79
4.33 4.63 3.85
Bx496XsFXiAlj.Eaeo 6.15 6.23 6.55
6.26 6.24 5.99
W38p0ogk.wIBVRXllY 7.13 7.35 7.55
7.37 7.36 7.55
QIBkqIS9LR5DfTlTS8 6.27 6.73 6.45
5.39 4.75 4.96
BZKiEvS0eQ305U0v34 6.35 7.02 6.76
5.45 5.25 5.02
6TheVd.HiE1UF3lX6g 5.53 5.02 5.36
5.61 5.66 5.37
So it is a matrix with gene names ex. Ku8QhfS0n_hIOABXuE, and subjects
named ex. NoD_14381_norm.1
How to do 1000 permutations of these 132 columns and on each created
new permuted matrix perform this code:
subject="all_replicate"
targets<-readTargets(paste(PhenotypeDir,"hg_sg_",subject,"_target.txt", sep=''))
Treat <- factor(targets$Treatment,levels=c("C","T"))
Replicates <- factor(targets$rep)
design <- model.matrix(~Replicates+Treat)
corfit <- duplicateCorrelation(dat, block = targets$Subject)
corfit$consensus.correlation
fit
<-lmFit(dat,design,block=targets$Subject,correlation=corfit$consensus.correlation)
fit<-eBayes(fit)
qval.cutoff=0.1; FC.cutoff=0.17
y1=topTable(fit, coef="TreatT", n=nrow(genes),adjust.method="BH",genelist=genes)
y1 for each iteration of permutation would have P.Value column and
these I would have plotted on the end to find the distribution of all
p values generated in those 1000 permutations.
Please advise,
Ana
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