Hi I am runing EdgeR to make an exact t-test (Iwould like to do a pairwise cmparaison but I am still not sure about that).
So here is to way I can compare time 0 versus time 8 for the positive responder on the experiment.
In one case I am normalising with all my data, then I filter all those who respond positively
In the other case I just take the positive responder for normalisation.
Of course the first case seems better but I would like to have your comments ...
Question : Would you take some data from another experiment to help data normalisation ?
Question : Would you give me a tips to use subject number to pair my sample over time ?
So here is to way I can compare time 0 versus time 8 for the positive responder on the experiment.
In one case I am normalising with all my data, then I filter all those who respond positively
In the other case I just take the positive responder for normalisation.
Of course the first case seems better but I would like to have your comments ...
Question : Would you take some data from another experiment to help data normalisation ?
Question : Would you give me a tips to use subject number to pair my sample over time ?
Code:
miRNA=read.table("merged_filtered_data.csv", header=TRUE,row.names=1) roundmiRNA=round(miRNA) info=read.table("samples_infos.csv", header=TRUE,row.names=1) miRNAdesign=data.frame(row.names = colnames(info), week=t(info)[,"week"], subject=t(info)[,"sub"], response=t(info)[,"MARSD"] ) is_responder=miRNAdesign$response==1 #first way to do it y <- DGEList(counts=roundmiRNA,group=miRNAdesign$week) y <- calcNormFactors(y) y <- estimateCommonDisp(y, verbose=TRUE) y <- estimateTagwiseDisp(y,trend="none") et <- exactTest(y[,is_responder]) topTags(et) #second way to do it y <- DGEList(counts=roundmiRNA[,is_responder],group=miRNAdesign$week[is_responder]) y <- calcNormFactors(y) y <- estimateCommonDisp(y, verbose=TRUE) y <- estimateTagwiseDisp(y,trend="none") et <- exactTest(y) topTags(et)
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