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Author Spotlight: Emerging Technologies and Advanced Tools for Decoding Metabolomics Data Analysis

Author Spotlight: Emerging Technologies and Advanced Tools for Decoding Metabolomics Data Analysis

Published on: November 10, 2023

SCNIC: Sparse correlation network investigation for compositional data.

Michael Shaffer¹, Kumar Thurimella^1,2, John D Sterrett³

¹Department of Biomedical Informatics, University of Colorado Anschutz Medical Campus, Aurora, Colorado, USA.

Molecular Ecology Resources

|August 24, 2022

Summary

This summary is machine-generated.

SCNIC software enhances microbiome study power by identifying and summarizing correlated microbial modules. This aids in discovering functional relationships and shared environmental drivers within complex datasets.

Keywords:

bioinformatics/phyloinformatics microbial ecology network analysis species interactions

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Area of Science:

Microbiology
Bioinformatics
Computational Biology

Background:

Microbiome studies face statistical power limitations due to small sample sizes and high feature dimensionality.
Correlative analyses of multi-omic microbiome data are particularly challenging.
Dimensionality reduction, by summarizing correlated observations into modules, can increase statistical power and reveal biological insights.

Purpose of the Study:

To develop open-source software, Sparse Cooccurrence Network Investigation for compositional data (SCNIC), for generating correlation networks and detecting/summarizing feature modules.
To provide methods for increasing statistical power and identifying functional relationships in microbiome and multi-omic data.
To introduce a novel Shared Minimum Distance (SMD) algorithm for module detection and compare it with the Louvain Modularity Maximization (LMM) algorithm.

Main Methods:

Developed SCNIC software for correlation network generation and module detection.
Implemented two module detection algorithms: Louvain Modularity Maximization (LMM) and a novel Shared Minimum Distance (SMD) algorithm.
Applied SCNIC to analyze two published microbiome datasets.

Main Results:

SCNIC successfully generated correlation networks and identified modules of highly correlated features.
Application of SCNIC led to increased statistical power in analyzing microbiome data.
Identified microbes that not only differed across groups but also exhibited strong correlations, suggesting shared drivers or cooperative interactions.

Conclusions:

SCNIC is an effective tool for generating correlation networks, identifying, and summarizing feature modules for downstream analysis.
The software increases statistical power and facilitates the identification of functional microbial relationships in microbiome and multi-omic data.
SCNIC's applicability extends beyond microbiome data to other data types like metabolomics, enabling multi-omic data integration.