Microorganisms (Sep 2024)

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

  • Meng Zhang,
  • Xiang Li,
  • Adelumola Oladeinde,
  • Michael Rothrock,
  • Anthony Pokoo-Aikins,
  • Gregory Zock

DOI
https://doi.org/10.3390/microorganisms12091866
Journal volume & issue
Vol. 12, no. 9
p. 1866

Abstract

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Networks are widely used to represent relationships between objects, including microorganisms within ecosystems, based on high-throughput sequencing data. However, challenges arise with appropriate statistical algorithms, handling of rare taxa, excess zeros in compositional data, and interpretation. This work introduces a novel Slope-Matrix-Graph (SMG) algorithm to identify microbiome correlations primarily based on slope-based distance calculations. SMG effectively handles any proportion of zeros in compositional data and involves: (1) searching for correlated relationships (e.g., positive and negative directions of changes) based on a “target of interest” within a setting, and (2) quantifying graph changes via slope-based distances between objects. Evaluations on simulated datasets demonstrated SMG’s ability to accurately cluster microbes into distinct positive/negative correlation groups, outperforming methods like Bray–Curtis and SparCC in both sensitivity and specificity. Moreover, SMG demonstrated superior accuracy in detecting differential abundance (DA) compared to ZicoSeq and ANCOM-BC2, making it a robust tool for microbiome analysis. A key advantage is SMG’s natural capacity to analyze zero-inflated compositional data without transformations. Overall, this simple yet powerful algorithm holds promise for diverse microbiome analysis applications.

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