2-Way k-Means as a Model for Microbiome Samples. (5th September 2017)

• Record Type: Journal Article
• Authors: Jackson, Weston J.; Agarwal, Ipsita; Pe'er, Itsik
• Other Names: Tafti Ahmad P. Academic Editor.
• Is Part Of: Journal of healthcare engineering. Volume 2017(2017)
• Publication Date: 2017-09-05
• Subjects: Hospital buildings -- Environmental engineering -- Periodicals; Medical technology -- Periodicals; Medical informatics -- Periodicals; 610.28
• DOI: 10.1155/2017/5284145
• Languages: English
• ISSNs: 2040-2295

Abstract

Abstract : 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.