Through three data examples, we demonstrate how to interpret the features identified by NMF to draw meaningful biological conclusions and discover hitherto unidentified patterns in the data.Ĭomparing whole metagenomes of various mammals, (Muegge et al., Science 332:970–974, 2011), the biosynthesis of macrolides pathway is found in hindgut-fermenting herbivores, but not carnivores. The relevance of the subcommunities identified by NMF is demonstrated by their excellent performance for classification. We apply the existing unsupervised NMF method and also develop a new supervised NMF method for extracting interpretable information from classification problems. Our methods can be applied to both OTU data and functional metagenomic data. The focus of this paper is on methods for extracting the subcommunities from the data, in particular Non-Negative Matrix Factorization (NMF). ![]() ![]() We view microbial communities as consisting of many subcommunities which are formed by certain groups of microbes functionally dependent on each other. Learning the structure of microbial communities is critical in understanding the different community structures and functions of microbes in distinct individuals.
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