Modelling of zero-inflation improves inference of metagenomic gene count data
Journal article, 2019

Metagenomics enables the study of gene abundances in complex mixtures of microorganisms and has become a standard methodology for the analysis of the human microbiome. However, gene abundance data is inherently noisy and contains high levels of biological and technical variability as well as an excess of zeros due to non-detected genes. This makes the statistical analysis challenging. In this study, we present a new hierarchical Bayesian model for inference of metagenomic gene abundance data. The model uses a zero-inflated overdispersed Poisson distribution which is able to simultaneously capture the high gene-specific variability as well as zero observations in the data. By analysis of three comprehensive datasets, we show that zero-inflation is common in metagenomic data from the human gut and, if not correctly modelled, it can lead to substantial reductions in statistical power. We also show, by using resampled metagenomic data, that our model has, compared to other methods, a higher and more stable performance for detecting differentially abundant genes. We conclude that proper modelling of the gene-specific variability, including the excess of zeros, is necessary to accurately describe gene abundances in metagenomic data. The proposed model will thus pave the way for new biological insights into the structure of microbial communities.



environmental sequencing


Markov chain Monte Carlo

Bayesian modeling

generalized linear models

human microbiome


Viktor Jonsson

Chalmers, Mathematical Sciences


Tobias Österlund

Chalmers, Mathematical Sciences, Applied Mathematics and Statistics

Olle Nerman

Chalmers, Mathematical Sciences, Applied Mathematics and Statistics

Erik Kristiansson

Chalmers, Mathematical Sciences, Applied Mathematics and Statistics

Statistical Methods in Medical Research

0962-2802 (ISSN)

Vol. 28 12 3712-3728

Subject Categories

Bioinformatics (Computational Biology)

Probability Theory and Statistics

Areas of Advance

Life Science Engineering (2010-2018)





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