A two-way multi-dimensional mixture model for clustering metagenomic sequences

Motivation: A major challenge facing metagenomics is the development of tools for the characterization of functional and taxonomic content of vast amounts of short metagenome reads. The efficacy of clustering methods depends on the number of reads in the dataset, the read length and relative abundances of source genomes in the microbial community. Results: In this paper, we formulate an unsupervised naive Bayes multi-species, multi-dimensional mixture model for reads from a metagenome. We use the proposed model to cluster metagenomic reads by their species of origin and to characterize the abundance of each species. We model the distribution of word counts along a genome as a Gaussian for shorter, frequent words and as a Poisson for longer words that are rare. We employ either a mixture of Gaussians or mixture of Poissons to model reads within each bin. An additional reason to use these distributions is their flexibility and ease of parameter estimation. Such a paradigm characterizes the compositional heterogeneity of the words along a genome, signifying its genome signature. Further, we handle the high-dimensionality and sparsity associated with the data, by grouping the set of words comprising the reads, resulting in a two-way mixture model. Finally, we derive an unsupervised Expectation Maximization algorithm for the models. Our method provides a general statistical framework for modeling metagenome reads. We demonstrate the accuracy and applicability of this method on simulated and real metagenomes. Our method can accurately cluster reads as short as 100 bps and estimate the species abundance as well. Our method outperforms LikelyBin, another unsupervised composition-based binning method for metagenomes, on datasets of varying abundances, divergences and read lengths.