The prevalence of human microbiome data in biomedical research has increased due to the association observed between microbiome composition and several diseases. The Dirichlet-multinomial distribution is frequently used to analyze this type of data, but it often fails to adequately model real microbiome datasets due to the restrictive parameterization imposed on its covariance matrix. This work proposes a novel distribution to be considered in microbiome data analysis, which can be used to define an alternative regression model for multivariate count data (e.g., microbiome data). The new distribution is a structured finite mixture model with Dirichlet-multinomial components. We show how this mixture model can enhance microbiome data analysis by clustering patients into “enterotypes”, a classification based on the taxa composition of gut microbiota. Finally, we consider an application based on a real gut microbiome dataset.

Ascari, R., Migliorati, S., Ongaro, A. (2023). An alternative to the Dirichlet-multinomial regression model for microbiome data analysis. In SEAS IN Book of short papers 2023 (pp.95-100).

An alternative to the Dirichlet-multinomial regression model for microbiome data analysis

Ascari, R
;
Migliorati, S;Ongaro, A
2023

Abstract

The prevalence of human microbiome data in biomedical research has increased due to the association observed between microbiome composition and several diseases. The Dirichlet-multinomial distribution is frequently used to analyze this type of data, but it often fails to adequately model real microbiome datasets due to the restrictive parameterization imposed on its covariance matrix. This work proposes a novel distribution to be considered in microbiome data analysis, which can be used to define an alternative regression model for multivariate count data (e.g., microbiome data). The new distribution is a structured finite mixture model with Dirichlet-multinomial components. We show how this mixture model can enhance microbiome data analysis by clustering patients into “enterotypes”, a classification based on the taxa composition of gut microbiota. Finally, we consider an application based on a real gut microbiome dataset.
slide + paper
Bayesian inference, Count data, Discrete simplex, Mixture model, Multivariate regression
English
SIS 2023 - Statistical Learning, Sustainability and Impact Evaluation
2023
SEAS IN Book of short papers 2023
9788891935618
2023
95
100
https://it.pearson.com/docenti/universita/partnership/sis.html#
none
Ascari, R., Migliorati, S., Ongaro, A. (2023). An alternative to the Dirichlet-multinomial regression model for microbiome data analysis. In SEAS IN Book of short papers 2023 (pp.95-100).
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10281/441699
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