Feature selection and causal analysis for microbiome studies in the presence of confounding using standardization.

ABSTRACT:

Background

Microbiome studies have uncovered associations between microbes and human, animal, and plant health outcomes. This has led to an interest in developing microbial interventions for treatment of disease and optimization of crop yields which requires identification of microbiome features that impact the outcome in the population of interest. That task is challenging because of the high dimensionality of microbiome data and the confounding that results from the complex and dynamic interactions among host, environment, and microbiome. In the presence of such confounding, variable selection and estimation procedures may have unsatisfactory performance in identifying microbial features with an effect on the outcome.

Results

In this manuscript, we aim to estimate populati

SUBMITTER: Goren E 

PROVIDER: S-EPMC8261956 | biostudies-literature | 2021 Jul

REPOSITORIES: biostudies-literature