Knowledge-based Integration of Multi-Omic Datasets with Anansi: Annotation-based Analysis of Specific Interactions

TL;DR

The paper introduces the anansi framework and R package, which uses external knowledge bases to improve multi-omics data integration by focusing on known interactions, thereby enhancing interpretability and statistical power.

Contribution

It presents a knowledge-based approach for multi-omics integration that constrains association testing to known interactions, reducing complexity and increasing power compared to all-vs-all methods.

Findings

Structured results are easier to interpret.

Increased statistical power by reducing hypothesis testing.

Application to host-microbe interactions demonstrates framework effectiveness.

Abstract

Motivation: Studies including more than one type of 'omics data sets are becoming more prevalent. Integrating these data sets can be a way to solidify findings and even to make new discoveries. However, integrating multi-omics data sets is challenging. Typically, data sets are integrated by performing an all-vs-all correlation analysis, where each feature of the first data set is correlated to each feature of the second data set. However, all-vs-all association testing produces unstructured results that are hard to interpret, and involves potentially unnecessary hypothesis testing that reduces statistical power due to false discovery rate (FDR) adjustment. Implementation: Here, we present the anansi framework, and accompanying R package, as a way to improve upon all-vs-all association analysis. We take a knowledge-based approach where external databases like KEGG are used to constrain the all-vs-all association hypothesis space, only considering pairwise associations that are a priori known to occur. This produces structured results that are easier to interpret, and increases statistical power by skipping unnecessary hypothesis tests. In this paper, we present the anansi framework and demonstrate its application to learn metabolite-function interactions in the context of host-microbe interactions. We further extend our framework beyond pairwise association testing to differential association testing, and show how anansi can be used to identify associations that differ in strength or degree based on sample covariates such as case/control status. Availability: https://github.com/thomazbastiaanssen/anansi