ZINQ-L: a zero-inflated quantile approach for differential abundance analysis of longitudinal microbiome data.

ABSTRACT:

Background

Identifying bacterial taxa associated with disease phenotypes or clinical treatments over time is critical for understanding the underlying biological mechanism. Association testing for microbiome data is already challenging due to its complex distribution that involves sparsity, over-dispersion, heavy tails, etc. The longitudinal nature of the data adds another layer of complexity - one needs to account for the within-subject correlations to avoid biased results. Existing longitudinal differential abundance approaches usually depend on strong parametric assumptions, such as zero-inflated normal or negative binomial. However, the complex microbiome data frequently violate these distributional assumptions, leading to inflated false discovery rates. In addition, the existi

SUBMITTER: Li S 

PROVIDER: S-EPMC11814158 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature