Hi Bert,
thanks for getting back to me. I have to permute those 132 columns
1000 times and perform the code given in the previous email.
Can you please show me how you would do that in the loop? This is also
a huge data set ...
Thanks
Ana
On Tue, Feb 4, 2020 at 2:34 PM Bert Gunter <bgunter.4...@gmail.com> wrote:
>
> If you just want to permute columns of a matrix,
>
> ?sample
> > sample.int(10)
> [1] 9 2 10 8 4 6 3 1 5 7
>
> and you can just use this as an index into the columns of your matrix,
> presumably within a loop of some sort.
>
> If I have misunderstood, just ignore.
>
> Cheers,
> Bert
>
>
>
>
> On Tue, Feb 4, 2020 at 12:23 PM Ana Marija <sokovic.anamar...@gmail.com>
> wrote:
>>
>> 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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>> and provide commented, minimal, self-contained, reproducible code.
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