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Modelling of zero-inflation improves inference of metagenomic gene count data

Artikel i vetenskaplig tidskrift
Författare Viktor Jonsson
Tobias Österlund
Olle Nerman
Erik Kristiansson
Publicerad i Statistical Methods in Medical Research
Volym 28
Nummer/häfte 12
Sidor 3712-3728
ISSN 0962-2802
Publiceringsår 2019
Publicerad vid Institutionen för matematiska vetenskaper
Sidor 3712-3728
Språk en
Länkar dx.doi.org/10.1177/0962280218811354
Ämnesord Metagenomics, human microbiome, environmental sequencing, Bayesian modeling, Markov chain Monte Carlo, MCMC, zero-inflation, generalized linear models, differential abundance analysis, poisson, regression, diversity, Health Care Sciences & Services, Mathematical & Computational Biology, Medical Informatics, Mathematics
Ämneskategorier Matematik, Medicinsk informatik

Sammanfattning

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.

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