anansi

anansi integrates multi-omics datasets by constraining association hypothesis space using external pathway databases, enabling structured pairwise association and differential association testing between features such as metabolites and microbial functions.

Key Features:

• Knowledge-based association constraint: Uses external databases such as KEGG to limit hypothesis testing to metabolite-function pairs recorded in pathway databases, reducing spurious associations and increasing statistical power by avoiding unnecessary tests.
• Differential association testing: Extends beyond pairwise association to identify associations that differ in strength or degree based on sample covariates such as case/control status.

Scientific Applications:

• Microbiome-metabolomics integration: Integrates microbiome and metabolomics data to corroborate patterns across datasets and discover metabolite-function interactions in host-microbe interaction studies.
• Multi-omics data integration: Provides an alternative to all-vs-all correlation analysis for integrating multiple omics data types, producing structured, interpretable results with improved statistical power.

Methodology:

The framework uses external databases such as KEGG to constrain the all-vs-all association hypothesis space, only considering pairwise associations that are a priori known to occur.