Detecting interaction networks in the human microbiome with conditional Granger causality.

ABSTRACT: Human microbiome research is rife with studies attempting to deduce microbial correlation networks from sequencing data. Standard correlation and/or network analyses may be misleading when taken as an indication of taxon interactions because "correlation is neither necessary nor sufficient to establish causation"; environmental filtering can lead to correlation between non-interacting taxa. Unfortunately, microbial ecologists have generally used correlation as a proxy for causality although there is a general consensus about what constitutes a causal relationship: causes both precede and predict effects. We apply one of the first causal models for detecting interactions in human microbiome samples. Specifically, we analyze a long duration, high resolution time series of the human microbiom

SUBMITTER: Mainali K 

PROVIDER: S-EPMC6544333 | biostudies-literature | 2019 May

REPOSITORIES: biostudies-literature