# Which (dis)similarity index to choose for cluster analysis?

I have data that refer to the number of occurrences of specific variable in samples:

       V1  V2  V3 ...
sample1 0   2   1
sample2 7   1   0
sample3 1   4   1
....


The data refers to the occurrence of genes(V1...) in different genomes (sample1..).

I want to perform a cluster analysis combined with an heat map. I used the function heatmap.2 in the gplot package in R. I used Euclidian distances for calculating the distance among the samples. The clustering algorithm is the default one for the function hclust in R (hclust(d, method = "complete", members = NULL)). However, I am not completely sure it is the right method. Any suggestion on how to choose the right method to calculate the distances among my samples?

EDIT The aim is to describe the distribution of the variables (genes) among the samples (genome), and cluster the samples(genomes) according with the values that each variables assume (meaning, how many specific genes are present)

• You need to describe your variables and state which clustering method you're using. Choice of distance or dissimilarity index tends to be application-specific. – shadowtalker May 4 '15 at 18:27
• @ssdecontrol Thanks for your comment. Please, see my edit. – efrem May 4 '15 at 18:32
• Great, I don't have time to add an answer right now but the question is now much more answerable than before, not to mention more helpful to future readers – shadowtalker May 4 '15 at 18:33
• Also, some info that might help you here, here, and maybe here – shadowtalker May 4 '15 at 18:34
• Might you be looking for something like this: immunityageing.com/content/5/1/5/figure/F2?highres=y – spdrnl May 15 '15 at 13:24