Estimating and testing the microbial causal mediation effect with high-dimensional and compositional microbiome data.

ABSTRACT:

Motivation

Recent microbiome association studies have revealed important associations between microbiome and disease/health status. Such findings encourage scientists to dive deeper to uncover the causal role of microbiome in the underlying biological mechanism, and have led to applying statistical models to quantify causal microbiome effects and to identify the specific microbial agents. However, there are no existing causal mediation methods specifically designed to handle high dimensional and compositional microbiome data.

Results

We propose a rigorous Sparse Microbial Causal Mediation Model (SparseMCMM) specifically designed for the high dimensional and compositional microbiome data in a typical three-factor (treatment, microbiome and outcome) causal study design. In par

SUBMITTER: Wang C 

PROVIDER: S-EPMC7867996 | biostudies-literature | 2020 Jan

REPOSITORIES: biostudies-literature