[BioC] Can we use Intraclass correlation (ICC) to optimize clustering parameters?

[Rafi [guest]]

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 

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