Evaluating microbial network inference methods: moving beyond synthetic data with reproducibility-driven benchmarks.

Area of Science:

• Microbiology
• Computational Biology
• Bioinformatics

Background:

• Microbial network inference is vital for understanding community interactions.
• Lack of validated gold standards hinders accurate evaluation of inferred microbial networks.
• Current methods often rely on synthetic data, which may not reflect real-world complexity.

Purpose of the Study:

• To comprehensively assess microbial network inference algorithms.
• To compare algorithm performance on real-world and synthetic datasets.
• To propose a robust evaluation framework for microbial network inference.

Main Methods:

• Evaluated six microbial network inference algorithms.
• Utilized four diverse real-world microbiome datasets.
• Compared performance using synthetic, noisy, and bootstrap-derived datasets.

Main Results:

• Bootstrap resampling and low-noise datasets (≤10% perturbation) reliably mimic real microbiome data properties.
• Synthetic datasets from SPIEC-EASI significantly diverge from real data.
• Several algorithms lack structural sensitivity, failing to differentiate structured from random networks.

Conclusions:

• Real-data-derived perturbations are essential for reliable network evaluation.
• Synthetic datasets require rigorous statistical validation before use.
• Robustness and reproducibility analyses are critical for microbial network inference method evaluation.