Statistical models and computational algorithms for discovering relationships in microbiome data

Shaikh Mateen R. and Beyene Joseph ()
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Shaikh Mateen R.: Department of Clinical Epidemiology and Biostatistics, McMaster University, 1280 Main Street West, Hamilton, Ontario, L8S 4L8, Canada
Beyene Joseph: Department of Clinical Epidemiology and Biostatistics, McMaster University, 1280 Main Street West, Hamilton, Ontario, L8S 4L8, Canada

Statistical Applications in Genetics and Molecular Biology, 2017, vol. 16, issue 1, 1-12

Abstract: Microbiomes, populations of microscopic organisms, have been found to be related to human health and it is expected further investigations will lead to novel perspectives of disease. The data used to analyze microbiomes is one of the newest types (the result of high-throughput technology) and the means to analyze these data is still rapidly evolving. One of the distributions that have been introduced into the microbiome literature, the Dirichlet-Multinomial, has received considerable attention. We extend this distribution’s use uncover compositional relationships between organisms at a taxonomic level. We apply our new method in two real microbiome data sets: one from human nasal passages and another from human stool samples.

Keywords: composition; constraints; Dirichlet-Multinomial; evolutionary algorithms; microbiome; model selection (search for similar items in EconPapers)
Date: 2017
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DOI: 10.1515/sagmb-2015-0096

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