A comparative analysis of mutual information methods for pairwise relationship detection in metagenomic data.

ABSTRACT:

Background

Construction of co-occurrence networks in metagenomic data often employs correlation to infer pairwise relationships between microbes. However, biological systems are complex and often display qualities non-linear in nature. Therefore, the reliance on correlation alone may overlook important relationships and fail to capture the full breadth of intricacies presented in underlying interaction networks. It is of interest to incorporate metrics that are not only robust in detecting linear relationships, but non-linear ones as well.

Results

In this paper, we explore the use of various mutual information (MI) estimation approaches for quantifying pairwise relationships in biological data and compare their performances against two traditional measures-Pearson's correlat

SUBMITTER: Francis D 

PROVIDER: S-EPMC11323399 | biostudies-literature | 2024 Aug

REPOSITORIES: biostudies-literature