I have a project with about 50-60 different samples with exome sequencing data. I have genotyped these samples and there are ~150 genes which have different levels of mutation ranging from missense, nonsense, indels, amplification, deletion, etc. I tiered them in terms of biological significance such that a 3 is significant impact, 2 has an impact, and 1 would be little impact. A sample w/o mutations at that gene had a 0.
I imported this into R and a df and tried to do classic clustering using hclust and made a few heatmaps/dendrograms. I used Ward.D2 for my analysis, but I'm not very skilled in statistics. I'm not sure if there would be a better algorithm for this dataset. Would anyone know a better method/algorithm? I'm trying to classify/group these samples using the exonic data I have.