Dear List, I have been trying to use limma to identify differentially expressed genes but have been finding some difilcuties in creating a basic model.matrix. and establishing a contrast. I usually have two different types of RNA in triplicates, a reference an an experimental and wish to see genes differentially expressed between the them. Following the limma manual my model.matrix would be: design <- model.matrix (~ -1 + factor (c (0,0,0,1,1,1))) Could it be done this way??? In such case, what would be the best way to fit a contrast between my reference and experimental??? Any help would be greatly appreciated! best regards, -- Marcos B. Pinho Programa de Engenharia Química - PEQ Laboratório de Engenharia de Cultivos Celulares- LECC Universidade Federal do Rio de Janeiro - UFRJ Instituto Nacional de Câncer - INCA Rio de Janeiro - Brasil [[alternative HTML version deleted]]

Hi Marcos, Marcos Pinho wrote: > Dear List, > I have been trying to use limma to identify differentially expressed genes > but have been finding some difilcuties in creating a basic model.matrix. and > establishing a contrast. I usually have two different types of RNA in > triplicates, a reference an an experimental and wish to see genes > differentially expressed between the them. Following the limma manual my > model.matrix would be: > > design <- model.matrix (~ -1 + factor (c (0,0,0,1,1,1))) > > Could it be done this way??? Yes, but you could use a simpler parameterization. > > In such case, what would be the best way to fit a contrast between my > reference and experimental??? If you use the treatment contrasts parameterization, the second coefficient will be the difference between the two sample types, so you don't need a contrasts matrix. design <- model.matrix(~ factor(rep(1:2, each = 3))) fit <- lmFit(<data>, design) fit2 <- eBayes(fit) topTable(fit2, coef = 2) > > Any help would be greatly appreciated! > > best regards, > > > -------------------------------------------------------------------- ---- > > _______________________________________________ > 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 -- James W. MacDonald, M.S. Biostatistician Douglas Lab University of Michigan Department of Human Genetics 5912 Buhl 1241 E. Catherine St. Ann Arbor MI 48109-5618 734-615-7826