Hi, For large data sets, hcluster will requires twice less memory than hclust (package amap). For even larger data sets, you can use xcluster program from Gavin Sherlock http://genetics. stanford.edu/~sherlock/cluster.html Package ctc has all tools dialog with this [free] software. And for visualization, I recommend TreeView or Freeview http://magix.fri.uni-lj.si/freeview But exploration on very large tree should be analysed carefully as each branch could be switch with another one like that: --- A == --- A +- B +- C + C + B Regards, Antoine Lucas. Le Mon, 13 Mar 2006 22:22:53 -0500 Sean Davis <sdavis2 at="" mail.nih.gov=""> a ?crit: > > > > On 3/13/06 21:08, "mark salsburg" <mark.salsburg at="" gmail.com=""> wrote: > > > I am having trouble getting the function heatmap() to work on the following > > gene expression > > > >> dim(SAMPLES_log) > > [1] 12626 20 > > > > > > sample1 sample2...................sample20 > > gen1 > > gen2 > > gen3 > > .... > > gen12626 > > > > > > > > I have converted SAMPLES_log to a numeric matrix using: > > > > as.matrix(SAMPLES_log) > > > > when I use the following command: > > > > heatmap(SAMPLES_log) > > > > Error: cannot allocate vector of size 622668 Kb > > In addition: Warning messages: > > 1: Reached total allocation of 1022Mb: see help(memory.size) > > 2: Reached total allocation of 1022Mb: see help(memory.size) > > Mark, > > In order to do a heatmap on 12000 genes, a triangular matrix of size > 12000x12000/2 needs to be calculated. This is large and will often result > in the out-of-memory error that you see. I don't often find that clustering > that many genes is meaningful in any major way, particularly since you will > be including a large number of genes that do not vary in the samples. If > you really need to do this, I would suggest that you use an external program > like cluster/treeview, as they may be somewhat less memory-hungry than R > (but I haven't tested that directly). > > > Is there some library in BioConductor that will allow me to output a > > heatmap. I want to compare the expression of the first 10 samples with the > > last 10 samples. > > If you want to do an unsupervised clustering of samples, use just hclust. > > If you want to do an unsupervised clustering of samples AND genes, I would > suggest reducing the number of genes using a filter for genes that show > variability (by using, say, the top 500 genes when sorted by coefficient of > variation, for example). In other words, there is no need to include a gene > in a heatmap that is the same for all samples. > > Ultimately, though, if you want to compare gene expression in two groups of > samples, you are asking a question that is best answered using a supervised > method, like a t-test. There are numerous ways to do a t-test between two > groups including the limma, siggenes, and multtest packages. > > Hope that helps. > > Sean > > _______________________________________________ > Bioconductor mailing list > Bioconductor at stat.math.ethz.ch > https://stat.ethz.ch/mailman/listinfo/bioconductor > -- Antoine Lucas Centre de g?n?tique Mol?culaire, CNRS 91198 Gif sur Yvette Cedex Tel: (33)1 69 82 38 89 Fax: (33)1 69 82 38 77