No, the unbalanced sample numbers will not cause edgeR to give asymmetric DE results. Quite the opposite. Since, miRNA results are expected to be globally assymmetric, I would expect edgeR to be under-stating rather than exaggerating the assymetry. BTW, please upgrade to the current release of R, Bioconductor and edgeR. Best wishes Gordon On Tue, 30 Apr 2013, Genomnia - Guffanti Alessandro wrote: > Dear colleagues: I am analyzing the result of some cancer samples miRNA NGS > experiments with edgeR, with the following setup: > > R version 2.15.3 (2013-03-01) > Platform: x86_64-w64-mingw32/x64 (64-bit) > > locale: > [1] LC_COLLATE=Italian_Italy.1252 LC_CTYPE=Italian_Italy.1252 > [3] LC_MONETARY=Italian_Italy.1252 LC_NUMERIC=C > [5] LC_TIME=Italian_Italy.1252 > > attached base packages: > [1] stats graphics grDevices utils datasets methods base > > other attached packages: > [1] edgeR_3.0.8 limma_3.14.4 > > I am following a rather standard workflow - please note that we have 8 > samples and only 1 control > >> targets <- read.delim("targets.txt", stringsAsFactors = FALSE) >> d <- readDGE(targets, header=FALSE) >> colnames(d) <- > c("ARMS1","ARMS2","ARMS3","ARMS4","ERMS1","ERMS2","ERMS3","ERMS4","N MS") >> dim(d) > [1] 2038 9 >> keep <- rowSums(cpm(d)> 5) >= 3 >> dim(d) > [1] 685 9 >> d$samples$lib.size <- colSums(d$counts) >> d<-calcNormFactors(d) >> d <- estimateCommonDisp(d, verbose=TRUE) > Disp = 0.71417 , BCV = 0.8451 >> d <- estimateTagwiseDisp(d, trend="none",verbose=TRUE) > Using interpolation to estimate tagwise dispersion. >> de.com <- exactTest(d) >> sumde.com$table$PValue < 0.05) > [1] 97 >> topValues <- topTagsde.com,n=97) >> summary(decideTestsDGEde.com,p.value=0.05)) > [,1] > -1 44 > 0 641 > 1 0 > > What we noticed is that there is a strong asimmetry in the corrected P > values, in that only the downregulated miRNAs have a significant corrected P > value - the upregulated miRNAs are less when examing the uncorreetd counts, > basically we have alf of the CPM > > Questions: > > => is the unbalanced experimental design affecting the results ? this > unbalance is coherent with the literature, in cancers the majority of miRNAs > are downregulated > > => if yes, can I correct it or we should just take the results as they are > and validate extensively if we want to explore also the upregulated miRNAs ? > > Thanks a lot in advance for any help, > > Alessandro & colleagues > ----------------------------------------------------- > Alessandro Guffanti - Head, Bioinformatics > Genomnia srl > Via Nerviano, 31/B ??? 20020 Lainate (MI) > Tel. +39-0293305.702 / Fax +39-0293305.777 > www.genomnia.com [http://www.genomnia.com/] > alessandro.guffanti at genomnia.com [mailto:alessandro.guffanti at genomnia.com] ______________________________________________________________________ The information in this email is confidential and intend...{{dropped:5}}