Dear Gordon, Thank you so much for the reference. I read all the chapter regarding to the models and I tried to set up the following code considering a data frame like this: > target Sample Variety Location 1 1 CS Mont 2 2 CS Mont 3 25 CS Bol 4 26 CS Bol 5 49 CS Ric 6 50 CS Ric 7 13 SG Mont 8 14 SG Mont 9 37 SG Bol 10 38 SG Bol 11 61 SG Ric 12 62 SG Ric > group <- factor(paste(target$Variety,target$Location,sep="_")) > cbind(target,Group=group) > d <- DGEList(counts=file,group=group) > DGEnorm <- calcNormFactors(d) > design <- model.matrix(~0+group, data=DGEnorm$samples) > colnames(design) <- levels(group) Which gave me the design matrix: > design CS_Bol CS_Mont CS_Ric SG_Bol SG_Mont SG_Ric CS_Mont 0 1 0 0 0 0 CS_Mont.1 0 1 0 0 0 0 CS_Bol 1 0 0 0 0 0 CS_Bol.1 1 0 0 0 0 0 CS_Ric 0 0 1 0 0 0 CS_Ric.1 0 0 1 0 0 0 SG_Mont 0 0 0 0 1 0 SG_Mont.1 0 0 0 0 1 0 SG_Bol 0 0 0 1 0 0 SG_Bol.1 0 0 0 1 0 0 SG_Ric 0 0 0 0 0 1 SG_Ric.1 0 0 0 0 0 1 attr(,"assign") [1] 1 1 1 1 1 1 attr(,"contrasts") attr(,"contrasts")$group [1] "contr.treatment" And then I estimated the trended and tag wise dispersion and fit the model doing: > disp.tren <- estimateGLMTrendedDisp(DGEnorm,design) > disp.tag <- estimateGLMTagwiseDisp(disp.tren,design) > fit <- glmFit(disp.tag,design) When I made some contrasts to find DE miRNAs, for example: > my.constrasts <- makeContrasts(CS_BolvsMont = CS_Bol-CS_Mont, CSvsSG_BolvsMont = (CS_Bol-CS_Mont)-(SG_Bol-SG_Mont), levels=design) > lrt <- glmLRT(fit, contrast=my.constrasts[,"CS_BolvsMont"]) I expected to find DE miRNAs due the environment effect (CS_BolvsMont) and for example DE miRNAs due the interaction genotypeXenvironment ( CSvsSG_BolvsMont). However the results do not seems to reflect it, since I did not get even a single DE miRNA with significant FDR (even less than 20%!!!!) and going back to the counts in the raw data I find reasonable differences in their expression, which was expected. I forgot to mention that I decided to consider stage by stage separately and not add one more factor on the model, since I am not interested, for the moment, on the time course (as I wrote in the previous email - see below). Could you (or any body else from the list) give me some advise regarding the code? Is this matrix appropriate for the kind of comparisons I am interested on? Thank you in advance for any input. Daniela 2012/10/30 Gordon K Smyth <smyth@wehi.edu.au> > Dear Daniela, > > edgeR can work with any design matrix. Just setup your interaction model > using standard R model formula. See for example Chapter 11 of: > > http://cran.r-project.org/doc/**manuals/R-intro.pdf<http: cran.r-="" project.org="" doc="" manuals="" r-intro.pdf=""> > > Best wishes > Gordon > > Date: Mon, 29 Oct 2012 16:24:31 +0100 >> From: Daniela Lopes Paim Pinto <d.lopespaimpinto@sssup.it> >> To: bioconductor@r-project.org >> Subject: [BioC] How to design matrix on edgeR to study genotype x >> environmental interaction >> >> Dear all, >> >> I'm currently working with data coming from deep sequencing of 48 small >> RNAs libraries and using edgeR to identify DE miRNAs. >> I could not figure out how to design my matrix for the following >> experimental design: >> >> I have 2 varieties (genotypes), cultivated in 3 different locations >> (environments) and collected in 4 physiological stages. None of them >> represent a control treatment. I'm particulary interested on identifying >> those miRNAs which modulate their expression dependent on genotypes (G), >> environments (E) and G x E interaction. For instance the same variety in >> the 3 different locations, both varieties in the same location and both >> varieties in the 3 different locations. >> >> I was wondering if I could use the section 3.3 of edgeR user guide as >> reference or if someone could suggest me any other alternative method. >> >> Thanks in advance >> >> Daniela >> > > ______________________________**______________________________**____ ______ > The information in this email is confidential and inte...{{dropped:10}}