Modelling of zero-inflation improves inference of metagenomic gene count data
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. In this study a new statistical model is presented which has, compared to other methods, a higher performance to identify genes that changes in abundance. These results will improve the ability to study antibiotic resistance genes in bacterial communities.