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An Analytical Tool-box for Comprehensive Biochemical, Structural and Transcriptome Evaluation of Oral Biofilms Mediated by Mutans Streptococci

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A Novel Slope-Matrix-Graph Algorithm to Analyze Compositional Microbiome Data.

Meng Zhang¹, Xiang Li², Adelumola Oladeinde²

¹Department of Mathematics, University of North Georgia, 82 College Cir, Dahlonega, GA 30597, USA.

|September 28, 2024

Summary

A new Slope-Matrix-Graph (SMG) algorithm accurately identifies microbiome correlations and differential abundance, even with challenging zero-inflated data. This method offers improved sensitivity and specificity for microbiome analysis.

Keywords:

differential abundance analysis (DAA)graph theory microbiome rate of change slope-based distances zero-inflated compositional data

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Area of Science:

Microbiome research
Bioinformatics
Statistical modeling

Background:

Microbiome networks are crucial for understanding ecosystem dynamics, often derived from high-throughput sequencing.
Existing statistical methods struggle with challenges like rare taxa, excess zeros in compositional data, and interpretation.

Purpose of the Study:

To introduce a novel algorithm, Slope-Matrix-Graph (SMG), for identifying microbiome correlations.
To address limitations in handling zero-inflated compositional data and improve accuracy in microbiome analysis.

Main Methods:

The Slope-Matrix-Graph (SMG) algorithm uses slope-based distance calculations to identify correlated relationships (positive/negative) within microbiome data.
It quantifies graph changes by measuring slope-based distances between objects and effectively handles zero-inflated compositional data without transformations.

Main Results:

SMG accurately clusters microbes into positive/negative correlation groups, outperforming Bray-Curtis and SparCC in sensitivity and specificity on simulated datasets.
SMG showed superior accuracy in detecting differential abundance (DA) compared to ZicoSeq and ANCOM-BC2.

Conclusions:

The Slope-Matrix-Graph (SMG) algorithm is a robust and effective tool for microbiome analysis, particularly for zero-inflated compositional data.
SMG offers a simple yet powerful approach with promise for diverse applications in microbiome research.