The problem is considerable. We found the same thing when we followed RMA exactly until the median polish step, and substituted Huber's biweight for median polish. This produces a tiny difference in the expression values, and the same 40-50% overlap in the list. Such are the limitations of the methodology at this point. --Naomi At 04:09 AM 7/9/2005, Adaikalavan Ramasamy wrote: >Yes we often see poor overlaps. A 40 - 50 % overlap is considered >pretty good but rare unless you are considering the top 5 genes >in both list or something silly like that. > >To make a fair comparison, try comparing the lists when they are >both filtered by the same p-value cutoff or statistics rather than >arbitrarily choosing a numbers. > > >Further, two minor cosmetic points about your code > >1) If you look at your design matrix from > > strain = c("WT","WT","WT","Drug","Drug","Drug") > design = model.matrix(~factor(strain)) > colnames(design) = c("WT","Drug") > design > WT Drug >1 1 1 >2 1 1 >3 1 1 >4 1 0 >5 1 0 >6 1 0 > >the first column represents an intercept not WT. To get the >correct interpretation, you need to change the second line to > > design = model.matrix(~ -1 + factor(strain) ) > > >2) You do not need the force the rownames to numeric using >as.numeric() since intersect happily works with characters. > > x <- c("a", "b", "c") > y <- c("b", "c", "d") > intersect(x,y) >[1] "b" "c" > >But I do not think either of these point change your results. > > > > >On Fri, 2005-07-08 at 18:18 +0100, Emmanuel Levy wrote: > > Dear Bioconductor community, > > > > I've been looking for differentially expressed genes in C. elegans after a > > drug treatment. > > There are 3 replicates of each condition and 2 conditions in total (WT and > > Drug) > > I used limma combined with either rma or mas5. I find a very very poor > > overlap in the results: > > > > - example (i) only 24 of the 100 most differentially expressed genes > > obtained using rma are found in > > the 1000 most differentially expressed genes obtained using mas5 > > - example (ii) only 183 genes are common to the lists of the 1000 most > > differentially expressed genes > > found using both methods. > > (see piece of code at the end) > > > > Either > > 1/ I am missing something which I would'nt be surprised of, as my > expertise > > is very limited. > > > > In that case I am sorry for pointing out something irrelevant and thank > you > > in advance for telling > > me what I'm missing, > > > > 2/ The differences in the normalization methods are really at the > origin of > > the observed differences. > > In that case, how can I know which method is the best for my case study? > > Does a helpful paper exists > > which explains in simple words the strengths/weaknesses of each method? > > > > Thank you very much in advance for your help, > > > > Emmanuel > > > > -------------------------------------- CODE > > -------------------------------------- > > library(affy) > > library(limma) > > > > # Load data into Affybatch > > data = ReadAffy(widget=T) > > > > # Background correction / normalization > > eset.rma = rma(data) > > eset.mas = mas5(data) > > > > # Get Expression values > > exp.rma = exprs(eset.rma) > > exp.mas = exprs(eset.mas) > > > > # --- Look for differentially expressed genes using Limma package > > strain = c("WT","WT","WT","Drug","Drug","Drug") > > design = model.matrix(~factor(strain)) > > colnames(design) = c("WT","Drug") > > > > fit.rma = lmFit(eset.rma,design) > > fit.mas = lmFit(eset.mas,design) > > > > fit.rma.2 = eBayes(fit.rma) > > fit.mas.2 = eBayes(fit.mas) > > > > top.rma = as.numeric(rownames(topTable(fit.rma.2,n=1000))) > > top.mas = as.numeric(rownames(topTable(fit.mas.2,n=100))) > > length(intersect(top.rma,top.mas)) > > > [1] 24 > > > > top.rma = as.numeric(rownames(topTable(fit.rma.2,n=100))) > > top.mas = as.numeric(rownames(topTable(fit.mas.2,n=1000))) > > length(intersect(top.rma,top.mas)) > > > [1] 0 > > > > [[alternative HTML version deleted]] > > > > _______________________________________________ > > Bioconductor mailing list > > Bioconductor at stat.math.ethz.ch > > https://stat.ethz.ch/mailman/listinfo/bioconductor > > > >_______________________________________________ >Bioconductor mailing list >Bioconductor at stat.math.ethz.ch >https://stat.ethz.ch/mailman/listinfo/bioconductor Naomi S. Altman 814-865-3791 (voice) Associate Professor Dept. of Statistics 814-863-7114 (fax) Penn State University 814-865-1348 (Statistics) University Park, PA 16802-2111