2-Way k-Means as a Model for Microbiome Samples

Abstract EN

Motivation.

Microbiome sequencing allows defining clusters of samples with shared composition.

However, this paradigm poorly accounts for samples whose composition is a mixture of cluster-characterizing ones and which therefore lie in between them in the cluster space.

This paper addresses unsupervised learning of 2-way clusters.

It defines a mixture model that allows 2-way cluster assignment and describes a variant of generalized k-means for learning such a model.

We demonstrate applicability to microbial 16S rDNA sequencing data from the Human Vaginal Microbiome Project.