Dear all, I would like to ask which is, at the moment, the most popular method to identify differentially expressed genes for two colour cDNA microarrays. Is "limma" the method that one would "trust" more in terms of identifying DE genes and at the same time obtain a small number of false positives/negatives? Assuming the data are calibrated, do limma (if this is the best method) and simple t-test (test of the log transformed intensities) give very different results? Thank you, Makis ---------------------- E Motakis, Mathematics E.Motakis at bristol.ac.uk

I do not know whether there is consensus on the best method. Limma, SAM and t-tests (for 2 population problems) are popular, as a permuations tests such as found in multtest. SAM and Limma give similar results for simple two population and ANOVA problems if calibrated similarly, although in my experience SAM is more conservative. t-tests will give different answers because there are usually a large number of genes with very small variance, and the moderated denominator will render these non-significant. Personally, I usually use limma with single-channel analysis, because it is the most flexible and I think the model is reasonable. SAM is fine for reference designs with only 1 level of replication. If a t-test is appropriate, so are Limma and SAM. MAANOVA is another good option, and the documentation indicates it can handle more complex models than Limma. --Naomi At 08:24 AM 7/24/2006, E Motakis, Mathematics wrote: >Dear all, > >I would like to ask which is, at the moment, the most popular method to >identify differentially expressed genes for two colour cDNA microarrays. Is >"limma" the method that one would "trust" more in terms of identifying DE >genes and at the same time obtain a small number of false >positives/negatives? > >Assuming the data are calibrated, do limma (if this is the best method) and >simple t-test (test of the log transformed intensities) give very different >results? > >Thank you, >Makis > > > > >---------------------- >E Motakis, Mathematics >E.Motakis at bristol.ac.uk > >_______________________________________________ >Bioconductor mailing list >Bioconductor at stat.math.ethz.ch >https://stat.ethz.ch/mailman/listinfo/bioconductor >Search the archives: >http://news.gmane.org/gmane.science.biology.informatics.conductor 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