mbDecoda: a debiased approach to compositional data analysis for microbiome surveys

TL;DR

mbDecoda is a new method for analyzing microbiome data that corrects for biases in relative abundance measurements to better identify microbes linked to health or disease.

Contribution

mbDecoda introduces a model-based debiasing approach for microbiome compositional data using a zero-inflated negative binomial model and an EM algorithm.

Findings

mbDecoda outperforms existing methods in simulation studies and real-world data analysis.

The method effectively adjusts for confounding factors and corrects compositional bias.

It enables accurate absolute abundance analysis despite sparse and over-dispersed data.

Abstract

Potentially pathogenic or probiotic microbes can be identified by comparing their abundance levels between healthy and diseased populations, or more broadly, by linking microbiome composition with clinical phenotypes or environmental factors. However, in microbiome studies, feature tables provide relative rather than absolute abundance of each feature in each sample, as the microbial loads of the samples and the ratios of sequencing depth to microbial load are both unknown and subject to considerable variation. Moreover, microbiome abundance data are count-valued, often over-dispersed and contain a substantial proportion of zeros. To carry out differential abundance analysis while addressing these challenges, we introduce mbDecoda, a model-based approach for debiased analysis of sparse compositions of microbiomes. mbDecoda employs a zero-inflated negative binomial model, linking mean abundance to the variable of interest through a log link function, and it accommodates the adjustment for confounding factors. To efficiently obtain maximum likelihood estimates of model parameters, an Expectation Maximization algorithm is developed. A minimum coverage interval approach is then proposed to rectify compositional bias, enabling accurate and reliable absolute abundance analysis. Through extensive simulation studies and analysis of real-world microbiome datasets, we demonstrate that mbDecoda compares favorably with state-of-the-art methods in terms of effectiveness, robustness and reproducibility.

Figures