Document Type
Article
Publication Date
8-2-2025
Original Citation
Fuschi A,
Merlotti A,
Tran T,
Nguyen H,
Weinstock GM.
Correlation Measures in Metagenomic Data: The Blessing of Dimensionality Applied Sciences. 2025;15(15):8602.
Keywords
JGM
JAX Source
Applied Sciences. 2025;15(15):8602.
DOI
https://doi.org/10.3390/app15158602
Abstract
Microbiome analysis has revolutionized our understanding of various biological processes, spanning human health and epidemiology (including antimicrobial resistance and hor- izontal gene transfer), as well as environmental and agricultural studies. At the heart of microbiome analysis lies the characterization of microbial communities through the quantification of microbial taxa and their dynamics. In the study of bacterial abundances, it is becoming more relevant to consider their relationship, to embed these data in the framework of network theory, allowing characterization of features like node relevance, pathways, and community structure. In this study, we address the primary biases encoun- tered in reconstructing networks through correlation measures, particularly in light of the compositional nature of the data, within-sample diversity, and the presence of a high number of unobserved species. These factors can lead to inaccurate correlation estimates. To tackle these challenges, we employ simulated data to demonstrate how many of these is- sues can be mitigated by applying typical transformations designed for compositional data. These transformations enable the use of straightforward measures like Pearson’s correlation to correctly identify positive and negative relationships among relative abundances, espe- cially in high-dimensional data, without having any need for further corrections. However, some challenges persist, such as addressing data sparsity, as neglecting this aspect can result in an underestimation of negative correlations.
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