Hi Ayoob,

I think that depends a lot on the research hypothesis you're trying to investigate. For classifying tumors, you could either use mutations, expression levels or methylation, and SNPs would most likely not be that useful. But for a phylogenetic study, SNPs might be very useful, though. You should also consider to use a supervised clustering approach (especially if you know how many clusters to expect) like k-means clustering. If you give a more detailed description of your problem, I might be able to answer your question more thoroughly (e.g. recommend a couple of R functions for some preliminary analyses). E.g., even with hierarchical clustering, you have to decide which distance metric and linkage criterion you want use to build your dendrogram.

Best, Daniel

Dear Daniel...

I'm really grateful for your helpful comment. Our studies focused mainly on the assessing of the genetic and/or epigenetic diversity within and between different plant species, which can be directed through many practices. The epigenetic diversity was evaluated by analyzing the DNA methylation level with aid of MSAP technique. In some cases we may performed cluster analysis based on sequencing results of ITS region.

Kind Regards.