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Dirichlet multinomial mixtures: generative models for microbial metagenomics

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Record title

Dirichlet multinomial mixtures: generative models for microbial metagenomics

Record identifier

TN_cdi_plos_journals_1323434142

Dirichlet multinomial mixtures: generative models for microbial metagenomics

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https://collection.sl.nsw.gov.au/record/TN_cdi_plos_journals_1323434142

Dirichlet multinomial mixtures: generative models for microbial metagenomics

Full title

Dirichlet multinomial mixtures: generative models for microbial metagenomics

Publisher

United States: Public Library of Science

Journal title

PloS one, 2012, Vol.7 (2), p.e30126-e30126

Record Identifier

TN_cdi_plos_journals_1323434142

Language

English

Formats

Publication information

Publisher

United States: Public Library of Science

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SCOPE AND CONTENTS

Contents

We introduce Dirichlet multinomial mixtures (DMM) for the probabilistic modelling of microbial metagenomics data. This data can be represented as a frequency matrix giving the number of times each taxa is observed in each sample. The samples have different size, and the matrix is sparse, as communities are diverse and skewed to rare taxa. Most methods used previously to classify or cluster samples have ignored these features. We describe each community by a vector of taxa probabilities. These vectors are generated from one of a finite number of Dirichlet mixture components each with different hyperparameters. Observed samples are generated through multinomial sampling. The mixture components cluster communities into distinct 'metacommunities', and, hence, determine envirotypes or enterotypes, groups of communities with a similar composition. The model can also deduce the impact of a treatment and be used for classification. We wrote software for the fitting of DMM models using the 'evidence framework' (http://code.google.com/p/microbedmm/). This includes the Laplace approximation of the model evidence. We applied the DMM model to human gut microbe genera frequencies from Obese and Lean twins. From the model evidence four clusters fit this data best. Two clusters were dominated by Bacteroides and were homogenous; two had a more variable community composition. We could not find a...

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Full title

Dirichlet multinomial mixtures: generative models for microbial metagenomics

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PRIMARY IDENTIFIERS

Record Identifier

TN_cdi_plos_journals_1323434142

Permalink

https://collection.sl.nsw.gov.au/record/TN_cdi_plos_journals_1323434142

OTHER IDENTIFIERS

ISSN

1932-6203

E-ISSN

1932-6203

DOI

10.1371/journal.pone.0030126

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