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Plants, animals and humans live in close association with microbial organisms. Increasingly, biologists have come to appreciate that microbes make up an important part of an organism's phenotype. This microbial community contains a unique complexity that makes it difficult to study their diversity. However, for many questions on the structure of the microbial community one only needs to know the relative order of diversity among samples rather than the total diversity. Unfortunately the culture of microorganisms can be complex but this has prompted the development of new scientific methodologies for their study. One of these methodologies is metagenomics. An important problem in metagenomics is measuring the dissimilarity between distributions of features, such as taxons or groups. The focus of this note is the proposal of a new method based on using Bhattacharyya distance and establishing a priori groups using the partitioning around medoids algorithm (PAM). The results reveal a good reduction in the size of the dataset and an interesting way of revealing possible subgroups “a priori” or communities among the microorganisms that make up the analyzed sample.

A Priori Groups Based On Bhattacharyya Distance And Partitioning Around Medoids Algorithm (PAM) With Applications To Metagenomics