A0600
Title: mbDecoda: A debiased approach to compositional data analysis for microbiome surveys
Authors: Tao Wang - Shanghai Jiao Tong University (China) [presenting]
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. 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, mbDecoda is introduced, 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. An expectation-maximization algorithm is developed to efficiently obtain maximum likelihood estimates of model parameters. 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, it is demonstrated that mbDecoda compares favorably to state-of-the-art methods in terms of effectiveness, robustness, and reproducibility.
