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

ABSTRACT: 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 demon

SUBMITTER: Zhang M 

PROVIDER: S-EPMC11434172 | biostudies-literature | 2024 Sep

REPOSITORIES: biostudies-literature