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Evaluating microbial network inference methods: moving beyond synthetic data with reproducibility-driven benchmarks
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DOI：10.1186/s12859-026-06510-3.png)
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Abstract
En 中文
Microbial network inference is an essential approach for revealing complex interactions within microbial communities. However, the lack of experimentally validated gold standards presents a significant obstacle in evaluating the biological accuracy of inferred networks. This study delivers a comprehensive comparative assessment of six widely used microbial network inference algorithms on four diverse real-world microbiome datasets alongside computationally generated samples, including synthetic, noisy, and bootstrap-derived variants. Our evaluation framework extends beyond conventional synthetic benchmarking by emphasizing reproducibility-focused assessments grounded in biologically realistic perturbations. Our analysis reveals that bootstrap resampling and low-level noisy datasets ($$\le $$10% perturbation) effectively preserve the key statistical properties of real microbiome data, serving as reliable proxies for assessing algorithmic consistency. Conversely, synthetic datasets generated via the widely used SPIEC-EASI method exhibit substantial divergence from real data. Notably, several algorithms fail to distinguish between structured and random networks, highlighting a lack of structural sensitivity and the limitations of overreliance on synthetic benchmarks. This study provides critical insights for the microbiome research community, emphasizing the need for more reliable and broadly applicable approaches to network evaluation. We propose a benchmarking framework that prioritizes real-data-derived perturbations and mandates rigorous statistical validation of synthetic datasets. Our findings highlight the importance of robustness and reproducibility analyses as complementary evaluation criteria for microbial network inference methods when validated biological ground truth is unavailable.
Keywords:
Microbiome network inference
Benchmarking
Bootstrap sampling
Noisy data
Reproducibility
Journal
IF:
3.3
Papers:
573
Citations:
5.2W