Dirichlet multinomial mixtures: generative models for microbial metagenomics.
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 communit...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2012-01-01
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| Series: | PLoS ONE |
| Online Access: | http://europepmc.org/articles/PMC3272020?pdf=render |
| _version_ | 1811196938921443328 |
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| author | Ian Holmes Keith Harris Christopher Quince |
| author_facet | Ian Holmes Keith Harris Christopher Quince |
| author_sort | Ian Holmes |
| collection | DOAJ |
| description | 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 significant impact of body mass on community structure. However, Obese twins were more likely to derive from the high variance clusters. We propose that obesity is not associated with a distinct microbiota but increases the chance that an individual derives from a disturbed enterotype. This is an example of the 'Anna Karenina principle (AKP)' applied to microbial communities: disturbed states having many more configurations than undisturbed. We verify this by showing that in a study of inflammatory bowel disease (IBD) phenotypes, ileal Crohn's disease (ICD) is associated with a more variable community. |
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| id | doaj.art-dd09f62bb0254f93ad4fc32265c87dd8 |
| institution | Directory Open Access Journal |
| issn | 1932-6203 |
| language | English |
| last_indexed | 2024-04-12T01:08:15Z |
| publishDate | 2012-01-01 |
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| series | PLoS ONE |
| spelling | doaj.art-dd09f62bb0254f93ad4fc32265c87dd82022-12-22T03:54:12ZengPublic Library of Science (PLoS)PLoS ONE1932-62032012-01-0172e3012610.1371/journal.pone.0030126Dirichlet multinomial mixtures: generative models for microbial metagenomics.Ian HolmesKeith HarrisChristopher QuinceWe 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 significant impact of body mass on community structure. However, Obese twins were more likely to derive from the high variance clusters. We propose that obesity is not associated with a distinct microbiota but increases the chance that an individual derives from a disturbed enterotype. This is an example of the 'Anna Karenina principle (AKP)' applied to microbial communities: disturbed states having many more configurations than undisturbed. We verify this by showing that in a study of inflammatory bowel disease (IBD) phenotypes, ileal Crohn's disease (ICD) is associated with a more variable community.http://europepmc.org/articles/PMC3272020?pdf=render |
| spellingShingle | Ian Holmes Keith Harris Christopher Quince Dirichlet multinomial mixtures: generative models for microbial metagenomics. PLoS ONE |
| title | Dirichlet multinomial mixtures: generative models for microbial metagenomics. |
| title_full | Dirichlet multinomial mixtures: generative models for microbial metagenomics. |
| title_fullStr | Dirichlet multinomial mixtures: generative models for microbial metagenomics. |
| title_full_unstemmed | Dirichlet multinomial mixtures: generative models for microbial metagenomics. |
| title_short | Dirichlet multinomial mixtures: generative models for microbial metagenomics. |
| title_sort | dirichlet multinomial mixtures generative models for microbial metagenomics |
| url | http://europepmc.org/articles/PMC3272020?pdf=render |
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