T-BAPS: A Bayesian Statistical Tool for Comparison of Microbial Communities Using Terminal-restriction Fragment Length Polymorphism (T-RFLP) Data

Tang Jing, Tao Jinglun, Urakawa Hidetoshi and Corander Jukka
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Tang Jing: University of Helsinki
Tao Jinglun: Ocean Research Institute, The University of Tokyo
Urakawa Hidetoshi: Ocean Research Institute, The University of Tokyo
Corander Jukka: Abo Akademi University

Statistical Applications in Genetics and Molecular Biology, 2007, vol. 6, issue 1, 21

Abstract: The investigation of microbial communities is an essential part of the study of the biosphere. Flexible molecular fingerprinting tools such as terminal-restriction fragment length polymorphism (T-RFLP) analysis are often applied in the studies to enable the characterization of the microbial population. However, such data have so far been primarily analyzed using conventional clustering methods. Here we introduce a Bayesian model-based method for the purpose of comparing microbial communities using T-RFLP data. Such datasets have in general several challenging features, e.g. sparseness, missing values and structurally zero-valued observations. These features are taken into account by developing a Bayesian latent class mixture model for the observations in our framework. To make inferences under the model we use a recent Markov chain Monte Carlo (MCMC) -based method for the Bayesian model selection. To assess the introduced method we analyze both simulated and real datasets. The simulations show that our approach compares preferably to standard statistical clustering tools, such as k-means, hierarchical clustering, and Autoclass. The developed tool is freely available as a software package T-BAPS at http://www.abo.fi/fak/mnf/mate/jc/software/t-baps.html.

Keywords: Bayesian modelling; Markov chain Monte Carlo; microbial communities; T-RFLP; unsupervised classification (search for similar items in EconPapers)
Date: 2007
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DOI: 10.2202/1544-6115.1303

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