Dear Ales, Thank you for your reply. If I ask myself the same question, I can see there is a lot of variation between groups. So, although I have 12 subjects per group and hence significant biological replicates, I am only finding one or two significant deferentially expressed genes. So the variability is looking like the reason I don't get nice heatmaps. The information I have is like log2 fold change. So the the CT of a gene, minus the CT of the control gene and this value to log2 scale. I think it is similar to log 2 fold change like MA in microarray. 2011/10/5 Ale? Maver <ales.maver at="" gmail.com="">: > Hi! > I think it depends on what information you have stored in rows and what in > columns. It also depends what data you have stored in the matrix. > For example, if your input values are deltaCt values (you mentioned you work > on RT-PCR results), they may differ substantially across genes, which may > cause your heatmap to look anomalous. > > If you look at an exemplar matrix with dCt values for three genes in rows > (Gene1-Gene3) and ten samples in columns: > mat<-matrix(c(rnorm(n=10, mean=5, sd=1), > ??? ??? ??? rnorm(n=10, mean=6, sd=1), > ??? ??? ??? rnorm(n=10, mean=15, sd=1)), ncol=10, byrow=T, > dimnames=list(paste("Gene", c(1:3), sep=""), paste("Sample", c(1:10), > sep=""))) > > Here the distributions of measured values differ substantially between genes > (means 5,10 and 15). If you inspect the plot where scaling across columns is > used: > heatmap(mat, scale="column") > > you will notice Gene3 to be always more yellow in color than Gene1 or Gene2, > as Gene3 values are always more than other two. > > However, using scaling across rows brings out the differences between > samples? - separately for each gene (row), as value range is calculated for > each gene (row) seperately: > heatmap(mat, scale="row") > > Hope this explains it, > Ales > > 2011/10/3 john herbert <arraystruggles at="" gmail.com=""> >> >> Dear Bioconductors, >> I am using the the heatmap.2 function to plot out a heatmap of 130 >> genes from Q-PCR expression. >> >> If I plot them out scaling by row, I get a heatmap that looks a bit >> blotchy. >> >> However, if I scale by column, the heatmap looks a lot better. >> >> The clustering of samples is the same but I don't really know the >> theory behind why column scaling looks better than row scaling. >> >> Please can someone advise me on any theory behind this? >> >> The data is in a matrix, like microarray, with columns of different >> conditions and rows of genes. >> >> Thank you, >> >> _______________________________________________ >> 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 > > > > -- > Ales Maver, MD > Institute of Medical Genetics, Department of Obstetrics and Gynaecology > UMC Ljubljana > ?lajmerjeva 3 > SI-1000 Ljubljana > Slovenia >