Filtering ASVs/OTUs via Mutual Information-Based Microbiome Network Analysis

TL;DR

This paper introduces a mutual information-based filtering method for microbiome data that effectively removes contaminants without significant information loss, outperforming traditional filtering approaches.

Contribution

The study presents a novel MI-based filtering strategy that uses information theory and graph analysis to improve contaminant removal in microbiome sequencing data.

Findings

MI-based filtering maintains true bacteria without significant information loss

It effectively detects and removes contaminants in microbial communities

The method does not require arbitrary thresholds and detects low-abundance taxa

Abstract

Microbial communities are widely studied using high-throughput sequencing techniques, such as 16S rRNA gene sequencing. These techniques have attracted biologists as they offer powerful tools to explore microbial communities and investigate their patterns of diversity in biological and biomedical samples at remarkable resolution. However, the accuracy of these methods can negatively affected by the presence of contamination. Several studies have recognized that contamination is a common problem in microbial studies and have offered promising computational and laboratory-based approaches to assess and remove contaminants. Here we propose a novel strategy, MI-based (mutual information based) filtering method, which uses information theoretic functionals and graph theory to identify and remove contaminants. We applied MI-based filtering method to a mock community data set and evaluated the amount of information loss due to filtering taxa. We also compared our method to commonly practice traditional filtering methods. In a mock community data set, MI-based filtering approach maintained the true bacteria in the community without significant loss of information. Our results indicate that MI-based filtering method effectively identifies and removes contaminants in microbial communities and hence it can be beneficial as a filtering method to microbiome studies. We believe our filtering method has two advantages over traditional filtering methods. First, it does not required an arbitrary choice of threshold and second, it is able to detect true taxa with low abundance.