Hi, I'm looking for a most commonly used microarray clustering method. The main problem is in deciding upon the appropriate amount of clusters to use in the clustering, both in the agglomerative and teh partitioning methods. I know there are certain procedures such as MSS or v-fold cross validation, which I might run. This would allow me to compare clusterings resulting from incrementing the K (amount of clusters to use). The main point is not only to find the best clustering algorithm, but a not less importantly, commonly used. Thanks, Ilya.

try this, http://www.mathstat.gsu.edu/%7Ematsnd/clustering/supp.htm http://www.mathstat.gsu.edu/%7Ematsnd/clustering/validation.txt Hi, > I'm looking for a most commonly used microarray clustering method. The > main problem is in deciding upon the appropriate amount of clusters to > use in the clustering, both in the agglomerative and teh partitioning > methods. > I know there are certain procedures such as MSS or v-fold cross > validation, which I might run. This would allow me to compare > clusterings resulting from incrementing the K (amount of clusters to use). > The main point is not only to find the best clustering algorithm, but a > not less importantly, commonly used. > > Thanks, > Ilya. > > _______________________________________________ > Bioconductor mailing list > Bioconductor@stat.math.ethz.ch > https://stat.ethz.ch/mailman/listinfo/bioconductor >

Dear Ilya, For better or for worse, the most commonly used method is agglomerative hierarchical clustering. For gene clustering, people then tend to prune the resulting tree very high -- that is, one often chooses K (= the number of clusters) to be much smaller than G (the number of genes/probes/etc). Given the output of a hierarchical algorithm, however, it is wiser to do this with a tree produced by a *divisive* algorithm. This choice produces a more stable result statistically, i.e. small perturbations in the input data tend to create 'small' perturbations in the output. An easy, well-established way to do this is to use the 'diana' function from the R 'cluster' package. Agglomerative methods are also, of course, available via 'hclust' (in mva) and 'agnes' (in cluster). Jenny Ilya Venger writes: > Hi, > I'm looking for a most commonly used microarray clustering method. The > main problem is in deciding upon the appropriate amount of clusters to > use in the clustering, both in the agglomerative and teh partitioning > methods. > I know there are certain procedures such as MSS or v-fold cross > validation, which I might run. This would allow me to compare > clusterings resulting from incrementing the K (amount of clusters to use). > The main point is not only to find the best clustering algorithm, but a > not less importantly, commonly used. > > Thanks, > Ilya. > > >