Hi all, I am having trouble viewing the full color spectrum (I only see "red") for data that I have clustered using the hopach method and displaying in either MapleTree or TreeView. When I use the golub or kidney data sets ("Bioconductor" examples) I have no problem seeing the different colors (green - red). The data set I am using is comprised of normalized (RMA) microarray data from 6 normal vs 30 ovarian tumor tissue samples that were orignially generated from Affymetrix HG_133A chip experiments and deposited in TCGA. After normalizing the raw CEL files I select the top 200 with the highest variance before using this data for clustering. Below is the code I am using. Could anyone tell me where I might be going wrong - i.e. why my cluster files (e.g. OvarianFzy.cdt, OvarianFzy.fct, OvarianFzy.mb; OvarianTree.atr, OvarianTree.cdt, OvarianTree.gtr) when opened in MapleTree or TreeView show only a red map as opposed to the green-red gradient ? I apologize in advance if my question is not clear, I am only a beginner at using the clustering methods. Thank you, Mark # Take the 200 genes with highest variance across the arrays in my normalized eset.r.m matrix. vars <- apply(eset.r.m, 1, var) selected <- vars > quantile(vars, (nrow(eset.r.m) - 200)/nrow(eset.r.m)) esetSub <- eset.r.m[selected, ] dim(esetSub) # create a table with only the probes selected and add coresponding gene names to column 2 of table (I do this using Excel until I can figure out how to do it in R) # this is only so that I can view the genes associated with the clustered data. write.table(selected, "C:\\temp\\JHU\\MicroArray_Analysis\\Bio_Rad\\Ovarian\\batch_9\\select ed.txt") # after adding gene symbols now read it back into R (this table has all of the selected probe IDs in column 1 and all of the corresponding gene symbols in column 2) esetSub.probes.desc <- read.table("C:\\temp\\JHU\\MicroArray_Analysis\\Bio_Rad\\Ovarian\\batc h_9\\esetSub.probes.desc.txt", sep = "\t") # now change the data type to character and mode to matrix esetSub.probes.desc <- as.character(esetSub.probes.desc) esetSub.probes.desc <- as.matrix(esetSub.probes.desc) library(hopach) # compute distance matrix gene.dist <- distancematrix(esetSub, d = "cosangle") dim(gene.dist) # run hopach to cluster genes gene.hobj <- hopach(esetSub, dmat = gene.dist) gene.hobj$clust$k # plot gene distance dplot(gene.dist, gene.hobj, ord = "final", main = "Ovarian:Gene Distance", showclusters = FALSE) # bootstrap resampling bobj <- boothopach(esetSub, gene.hobj, B = 100) bootplot(bobj, gene.hobj, ord = "bootp", main = "Ovarian cancer", showclusters = FALSE) # Clustering of arrays array.hobj <- hopach(t(esetSub), d = "euclid") array.hobj$clust$k # gene clustering and bootstrap results table makeoutput(esetSub, gene.hobj, bobj, file = "C:\\temp\\JHU\\MicroArray_Analysis\\Bio_Rad\\Ovarian\\batch_9\\Ovaria n.data\\ovarian.out", gene.names = esetSub.probes.desc[,2]) # bootstrap fuzzy clustering boot2fuzzy(esetSub, bobj, gene.hobj, array.hobj, file = "C:\\temp\\JHU\\MicroArray_Analysis\\Bio_Rad\\Ovarian\\batch_9\\Ovaria n.data\\OvarianFzy", gene.names = esetSub.probes.desc[,2]) # HOPACH hierarchical clustering in MapleTree hopach2tree(esetSub, file = "C:\\temp\\JHU\\MicroArray_Analysis\\Bio_Rad\\Ovarian\\batch_9\\Ovaria n.data\\data\\OvarianTree", hopach.genes = gene.hobj, hopach.arrays = array.hobj, dist.genes = gene.dist, gene.names = esetSub.probes.desc[,2]) -- Mark Baumeister http://sites.google.com/site/lfmmab/ [[alternative HTML version deleted]]