Hi all, I am trying to find co-expressed genes in my affy data. I use hierarchical clustering with dynamic tree cut. I want to choose optimal clustering/cut parameters and I am new to cluster validation. I understand that there are many cluster indices that can be used for cluster validation. Since I am interested in co-expression only, can I simply use intraclass correlation (ICC) as a metric to choose optimal parameters? ie, choose the clustering parameters that gives the highest ICC in each cluster. Is ICC commonly used for choosing clustering parameters? Is it Ok? or Is there any other more commonly used metric? Thanks a lot in advance. Rafi -- output of sessionInfo(): R version 3.0.2 (2013-09-25) Platform: x86_64-w64-mingw32/x64 (64-bit) locale: [1] LC_COLLATE=English_United States.1252 LC_CTYPE=English_United States.1252 LC_MONETARY=English_United States.1252 [4] LC_NUMERIC=C LC_TIME=English_United States.1252 attached base packages: [1] parallel stats graphics grDevices utils datasets methods base other attached packages: [1] GeneAnswers_2.4.0 RColorBrewer_1.0-5 Heatplus_2.8.0 MASS_7.3-29 XML_3.98-1.1 RCurl_1.95-4.1 [7] bitops_1.0-6 igraph_0.6.6 plyr_1.8 KEGG.db_2.10.1 GSEABase_1.24.0 rat2302.db_2.10.1 [13] org.Rn.eg.db_2.10.1 annotate_1.40.0 GOstats_2.28.0 graph_1.40.1 Category_2.28.0 Matrix_1.1-1.1 [19] GO.db_2.10.1 RSQLite_0.11.4 DBI_0.2-7 AnnotationDbi_1.24.0 Biobase_2.22.0 BiocGenerics_0.8.0 loaded via a namespace (and not attached): [1] AnnotationForge_1.4.4 genefilter_1.44.0 grid_3.0.2 IRanges_1.20.6 lattice_0.20-24 [6] RBGL_1.38.0 splines_3.0.2 stats4_3.0.2 survival_2.37-4 tools_3.0.2 [11] xtable_1.7-1 -- Sent via the guest posting facility at bioconductor.org.

you might find the 'WCGNA' package to be useful for a starting point. it is also extensively published and IIRC, at least one of the authors is on this list Statistics is the grammar of science. Karl Pearson <http: en.wikipedia.org="" wiki="" the_grammar_of_science=""> On Tue, Feb 11, 2014 at 11:22 AM, Rafi [guest] <guest@bioconductor.org>wrote: > > Hi all, > > I am trying to find co-expressed genes in my affy data. I use hierarchical > clustering with dynamic tree cut. I want to choose optimal clustering/cut > parameters and I am new to cluster validation. I understand that there are > many cluster indices that can be used for cluster validation. > > Since I am interested in co-expression only, can I simply use intraclass > correlation (ICC) as a metric to choose optimal parameters? ie, choose the > clustering parameters that gives the highest ICC in each cluster. > > Is ICC commonly used for choosing clustering parameters? Is it Ok? or Is > there any other more commonly used metric? > > Thanks a lot in advance. > Rafi > > > > -- output of sessionInfo(): > > R version 3.0.2 (2013-09-25) > Platform: x86_64-w64-mingw32/x64 (64-bit) > > locale: > [1] LC_COLLATE=English_United States.1252 LC_CTYPE=English_United > States.1252 LC_MONETARY=English_United States.1252 > [4] LC_NUMERIC=C LC_TIME=English_United > States.1252 > > attached base packages: > [1] parallel stats graphics grDevices utils datasets methods > base > > other attached packages: > [1] GeneAnswers_2.4.0 RColorBrewer_1.0-5 Heatplus_2.8.0 > MASS_7.3-29 XML_3.98-1.1 RCurl_1.95-4.1 > [7] bitops_1.0-6 igraph_0.6.6 plyr_1.8 > KEGG.db_2.10.1 GSEABase_1.24.0 rat2302.db_2.10.1 > [13] org.Rn.eg.db_2.10.1 annotate_1.40.0 GOstats_2.28.0 > graph_1.40.1 Category_2.28.0 Matrix_1.1-1.1 > [19] GO.db_2.10.1 RSQLite_0.11.4 DBI_0.2-7 > AnnotationDbi_1.24.0 Biobase_2.22.0 BiocGenerics_0.8.0 > > loaded via a namespace (and not attached): > [1] AnnotationForge_1.4.4 genefilter_1.44.0 grid_3.0.2 > IRanges_1.20.6 lattice_0.20-24 > [6] RBGL_1.38.0 splines_3.0.2 stats4_3.0.2 > survival_2.37-4 tools_3.0.2 > [11] xtable_1.7-1 > > -- > Sent via the guest posting facility at bioconductor.org. > > _______________________________________________ > Bioconductor mailing list > Bioconductor@r-project.org > https://stat.ethz.ch/mailman/listinfo/bioconductor > Search the archives: > http://news.gmane.org/gmane.science.biology.informatics.conductor > [[alternative HTML version deleted]]