Powerful and robust non-parametric association testing for microbiome data via a zero-inflated quantile approach (ZINQ).

ABSTRACT:

Background

Identification of bacterial taxa associated with diseases, exposures, and other variables of interest offers a more comprehensive understanding of the role of microbes in many conditions. However, despite considerable research in statistical methods for association testing with microbiome data, approaches that are generally applicable remain elusive. Classical tests often do not accommodate the realities of microbiome data, leading to power loss. Approaches tailored for microbiome data depend highly upon the normalization strategies used to handle differential read depth and other data characteristics, and they often have unacceptably high false positive rates, generally due to unsatisfied distributional assumptions. On the other hand, many non-parametric tests suffer fr

SUBMITTER: Ling W 

PROVIDER: S-EPMC8414689 | biostudies-literature | 2021 Sep

REPOSITORIES: biostudies-literature