Hello BioConductor world, I am doing a time course study using Affymetrix microarrays. I have three time points (0, 4, and 6 hours) and a control and treatment. There is no treatment at 0 hours because the genes would be the same. I am able to get the significant transcripts, but I can not create the heat maps I want. I have searched the literature and the internet and have not found what I was looking for. I would like two heat maps: one for the controls versus treatments for the different time points (two columns or groups, 4 and 6 hr) and the second heat map comparing the different times (three columns or groups, 0 to 4 and 6 hr and 4 to 6 hr). What would be the best way to do this? Is there a way to combine the the results from the different microarrays in the same control or treatment to form one column instead of the multiple columns? Looking at other heat maps, I could not find an example of the combined columns. I have about 200 different transcripts for the the controls versus treatments for the different time. Is there a way to select the number of genes in the heat map. I may be think about the heat maps wrong, so if I am what is the best way to think about heat maps and the code or instructions, for a new person, for the right type of heat map. Here is my code (control time [c0, c4, c6] or treatment time[t4,t6]): library(affy) library(limma) targets = readTargets("time course.txt") raw = ReadAffy(filenames=targets$FileName) data = read.AnnotatedDataFrame("time course.txt", sep="", fill=TRUE) phenoData(raw) = data[sampleNames(raw),] RMA = rma(raw) lev = c("c0","c4","t4","c6","t6") f = factor(data$Target, levels=lev) design = model.matrix(~0+f) colnames(design) = lev fit = lmFit(RMA, design) cont.wt = makeContrasts( "t4-c4", "t6-c6", c0t4 = c0-(t4-c4), c0t6 = c0-(t6-c6), t4t6 = (t4-c4)-(t6-c6), levels=design) fit2 = contrasts.fit(fit, cont.wt) fit2 = eBayes(fit2) results = classifyTestsF(fit2, p.value=0.05) summary(results) Thank you in advance. Andrew Block Graduate Student Nebraska Center of Virology University of Nebraska at Lincoln [[alternative HTML version deleted]]

Andrew, Try using heatmap.2 (in library gplots) to make a picture of the 100 genes with the lowest p value in whatever test you are using to assess significance. Something along these lines: heatmap.2 (exprs(RMA[<your genes="">,])) where <your genes=""> is list of the 100 genes with smallest p values. Best, Tom On Oct 10, 2011, at 1:06 PM, blockaa at huskers.unl.edu wrote: > Hello BioConductor world, > > > I am doing a time course study using Affymetrix microarrays. I have three time points (0, 4, and 6 hours) and a control and treatment. There is no treatment at 0 hours because the genes would be the same. I am able to get the significant transcripts, but I can not create the heat maps I want. I have searched the literature and the internet and have not found what I was looking for. I would like two heat maps: one for the controls versus treatments for the different time points (two columns or groups, 4 and 6 hr) and the second heat map comparing the different times (three columns or groups, 0 to 4 and 6 hr and 4 to 6 hr). What would be the best way to do this? Is there a way to combine the the results from the different microarrays in the same control or treatment to form one column instead of the multiple columns? Looking at other heat maps, I could not find an example of the combined columns. I have about 200 different transcripts for the the controls versus treatments ! > for the different time. Is there a way to select the number of genes in the heat map. I may be think about the heat maps wrong, so if I am what is the best way to think about heat maps and the code or instructions, for a new person, for the right type of heat map. > > > Here is my code (control time [c0, c4, c6] or treatment time[t4,t6]): > > > library(affy) > > library(limma) > > > targets = readTargets("time course.txt") > > raw = ReadAffy(filenames=targets$FileName) > > data = read.AnnotatedDataFrame("time course.txt", sep="", fill=TRUE) > > phenoData(raw) = data[sampleNames(raw),] > > RMA = rma(raw) > > > lev = c("c0","c4","t4","c6","t6") > > f = factor(data$Target, levels=lev) > > design = model.matrix(~0+f) > > colnames(design) = lev > > fit = lmFit(RMA, design) > > cont.wt = makeContrasts( > > "t4-c4", > > "t6-c6", > > c0t4 = c0-(t4-c4), > > c0t6 = c0-(t6-c6), > > t4t6 = (t4-c4)-(t6-c6), > > levels=design) > > fit2 = contrasts.fit(fit, cont.wt) > > fit2 = eBayes(fit2) > > > results = classifyTestsF(fit2, p.value=0.05) > > summary(results) > > > Thank you in advance. > > Andrew Block > Graduate Student > Nebraska Center of Virology > University of Nebraska at Lincoln > > [[alternative HTML version deleted]] > > _______________________________________________ > Bioconductor mailing list > Bioconductor at r-project.org > https://stat.ethz.ch/mailman/listinfo/bioconductor > Search the archives: http://news.gmane.org/gmane.science.biology.informatics.conductor >