If you consider a set of genomes, you are typically interested in comparison to identify similarities and differences between individual genomes. Consider a cohort of patients where you observed that a certain therapy works for 80% of them. The question arises, how does the genotype of the 20% differ from the 80% and what do they have in common.
We support various clustering algorithms, such as k-means or hierarchical clustering, to group individual genome data. If you want to verify your hypotheses, you can set the gene and locus coordinates to build the clusters. If you do not know, why these cohorts differ, you can start an automatic discovery. Location permutations are calculated on the fly and ranked by relevance.
Thus, the cohort analysis helps to discovery new coordinates to form clusters. It supports you to obtain new insights and to build new hypotheses. The results of the clustering are visualized as interactive diagrams.