I try to implement LIMMA for paired samples in order to compare gene expression before/after treatment. But...i'm not confident in my results since every single gene expression seems to be significantly associeted with the treatment!
Here is my methodology:
where df is a dataframe with: -1000 columns for different gene expression -+ 1 column 'id' (10 different id, repeated 2 times) -+ 1 column 'ttt' (0=before, 1=after treatment) -20 rows of observations (10 patients observed twice)
So once I get my matrix:
subject_id<-df$id A vector with 10 different id repeated 2 times
ttt<-as.factor(df$ttt) A vector with the treatment status (before/after)
Here is my design matrix:
colnames(design) <- levels(ttt)
As asked in limma() description i specify the correlations:
corfit <- duplicateCorrelation(mat,design,block=subject_id)
Fitting of the model:
fit<-lmFit(mat,design,block =subject_id,correlation = corfit$consensus)
Getting adjusted pvalues for every gene comparison before/after treatment:
But every single gene expression seems to be relevant, so i'm very doubtful about my methodology Any idea? Many thanks!