I have used the following code to find differentially expressed genes : based on affymetrix data when treating cells with water (controls) and ethanol (treatment). files <- c("W05h_1.CEL", "W0h_1.CEL", "W2h_1.CEL", "W2h_2.CEL", "W4h_1.CEL", "E2h_1.CEL", "E2h_2.CEL", "E2h_3.CEL", "E2h_4.CEL") Data <- ReadAffy(filenames = files) Data_gcrma <- gcrma(Data) design<- cbind(WAT=c(1,1,1,1,1,0,0,0,0), ETH=c(0,0,0,0,0,1,1,1,1)) design fit <- lmFit(Data_gcrma,design) cont.matrix <- makeContrasts(WATvsETH=ETH-WAT, levels=design) fit2<- contrasts.fit(fit,cont.matrix) fit3 <- eBayes(fit2) tab <- topTable(fit3, n=1500, adjust="fdr") genenames <- as.character(tab$ID) ll <- getLL(genenames, "drosgenome1") sym <- getSYMBOL(genenames, "drosgenome1") tab2 <- data.frame(sym,tab) diff_exp <- tab2[tab2$P<0.05 & tab2$M<0.0,] print("No down reg genes:"); length(diff_exp$ID) diff_exp <- tab2[tab2$P<0.05 & tab2$M>0.0,] print("No up reg genes:") ;length(diff_exp$ID) The question I have is about the topTable function. Does this now only produce a P.value column which contains the adjusted P-values and is this column tagged as "P"? Are the two lines of code that extract the genes with P values <0.05 correct? diff_exp <- tab2[tab2$P<0.05 & tab2$M<0.0,] print("No down reg genes:"); length(diff_exp$ID) previously I had code which used $adj.P.Val as a filter but this does not work any more. Thanks Sue Jones University of Sussex

Quoting Sue Jones <s.jones at="" sussex.ac.uk="">: > > > I have used the following code to find differentially expressed genes > : based on affymetrix data when treating cells with water (controls) and > ethanol (treatment). > > files <- c("W05h_1.CEL", "W0h_1.CEL", "W2h_1.CEL", > "W2h_2.CEL", "W4h_1.CEL", "E2h_1.CEL", "E2h_2.CEL", > "E2h_3.CEL", "E2h_4.CEL") > Data <- ReadAffy(filenames = files) > Data_gcrma <- gcrma(Data) > design<- cbind(WAT=c(1,1,1,1,1,0,0,0,0), ETH=c(0,0,0,0,0,1,1,1,1)) > design > > fit <- lmFit(Data_gcrma,design) > cont.matrix <- makeContrasts(WATvsETH=ETH-WAT, levels=design) > fit2<- contrasts.fit(fit,cont.matrix) > fit3 <- eBayes(fit2) > > tab <- topTable(fit3, n=1500, adjust="fdr") > genenames <- as.character(tab$ID) > ll <- getLL(genenames, "drosgenome1") > sym <- getSYMBOL(genenames, "drosgenome1") > tab2 <- data.frame(sym,tab) > > diff_exp <- tab2[tab2$P<0.05 & tab2$M<0.0,] > print("No down reg genes:"); length(diff_exp$ID) > > diff_exp <- tab2[tab2$P<0.05 & tab2$M>0.0,] > print("No up reg genes:") ;length(diff_exp$ID) > > > The question I have is about the topTable function. Does this now only > produce a P.value column which contains the adjusted P-values and is this > column tagged as "P"? Are the two lines of code that extract the genes > with P values <0.05 correct? > > diff_exp <- tab2[tab2$P<0.05 & tab2$M<0.0,] > print("No down reg genes:"); length(diff_exp$ID) > > > previously I had code which used $adj.P.Val as a filter but this does not > work any more. > > Thanks > > Sue Jones > University of Sussex Not sure which version of Limma you're using, but since version 2.9.3 (Oct'06)topTable has changed a couple of column headings: "M" is "logFC" now, and you're using "M" that on your code, so I think this may be the problem. You can use changeLog() to see changes in Limma. Jose -- Dr. Jose I. de las Heras Email: J.delasHeras at ed.ac.uk The Wellcome Trust Centre for Cell Biology Phone: +44 (0)131 6513374 Institute for Cell & Molecular Biology Fax: +44 (0)131 6507360 Swann Building, Mayfield Road University of Edinburgh Edinburgh EH9 3JR UK -- The University of Edinburgh is a charitable body, registered in Scotland, with registration number SC005336.