Dirichlet Multinomial Mixtures: Generative Models for Microbial Metagenomics

By I. Holmes, Keith Harris, Christopher Quince. PLoS ONE, 2012

805 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/837a7355d9e136eae80f6ced7a837d86b6299bc6

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Citations in the corpus

Estimating the Number of Species in Microbial Diversity Studies (2014, methodology): “These procedures include UniFrac (Lozupone et al. 2011, Holmes et al. 2012), methods of coinertia (Dray et al. 2003), and ordination methods such as correspondence analysis (e.g., Greenacre 2007); they are often combined with graphical techniques such as multidimensional scaling.”
Hypothesis Testing and Power Calculations for Taxonomic-Based Human Microbiome Data (2012, methodology): “In [17] the authors apply the Dirichlet-multinomial mixture for the probabilistic modeling of microbial metagenomics data, which was used to successfully cluster communities into groups with a similar composition.”
Effects of library size variance, sparsity, and compositionality on the analysis of microbiome data (2015, background): “…a distribution (Cameron & Trivedi), and there is debate about which distribution to use 201 (Auer & Doerge 2010; Cheung 2002; Connolly et al. 2009; Holmes et al. 2012; McMurdie & 202 Holmes 2014; Paulson et al. 2013; Rapaport et al. 2013; Soneson & Delorenzi 2013; White et al. 203 2009; Yu et al.…”
ANOVA-Like Differential Expression (ALDEx) Analysis for Mixed Population RNA-Seq (2013, background): “Others have also suggested that this distribution is appropriate for these types of data[33–35].”
Network analysis methods for studying microbial communities: A mini review (2021, methodology): “MDiNE addresses compositionality by utilizing a Dirichlet-multinomial logistic-normal distribution model [61,62].”
Applications of Machine Learning in Human Microbiome Studies: A Review on Feature Selection, Biomarker Identification, Disease Prediction and Treatment (2021, background): “To cluster groups of communities with similar compositions into envirotypes or enterotypes and thus into “metacommunities” the Dirichlet multinomial mixture (DMM) generative modeling framework has been developed (Holmes et al., 2012).”
PyMC: a modern, and comprehensive probabilistic programming framework in Python (2023, background): “Models like this one are important in a variety of areas, including natural language processing ( Madsen, Kauchak & Elkan, 2005 ), ecology ( Harrison et al., 2019 ), and Genomics ( Holmes, Harris & Quince, 2012 ; Nowicka & Robinson, 2016 ).”
Unifying the analysis of high-throughput sequencing datasets: characterizing RNA-seq, 16S rRNA gene sequencing and selective growth experiments by compositional data analysis (2014, methodology): “Several groups have recently shown that the sampling error inherent in high-throughput sequencing protocols can be modeled appropriately by Monte Carlo sampling from a Dirichlet distribution [8,9,26,27].”