Unfolding and De-confounding: Biologically meaningful causal inference from longitudinal multi-omic networks using METALICA.

A key challenge in the analysis of microbiome data is the integration of multi-omic datasets and the discovery of interactions between microbial taxa, their expressed genes, and the metabolites they consume and/or produce. In an effort to improve the state-of-the-art in inferring biologically meaningful multi-omic interactions, we sought to address some of the most fundamental issues in causal inference from longitudinal multi-omics microbiome data sets. We developed METALICA, a suite of tools and techniques that can infer interactions between microbiome entities. METALICA introduces novel We have developed a suite of tools and techniques capable of inferring interactions between microbiome entities. METALICAintroduces novel techniques called unrolling and de-confounding that are employed to uncover multi-omic entities considered to be confounders for some of the relationships that may be inferred using standard causal inferencing tools. To evaluate our method, we conducted tests on the Inflammatory Bowel Disease (IBD) dataset from the iHMP longitudinal study, which we pre-processed in accordance with our previous work.