rCCLasso: a robust framework for microbial correlation network analysis reveals age-related microbial dynamics.

Introduction: The human gut microbiome continues to evolve beyond early adulthood, yet most microbiome aging studies focus on changes in individual taxa or overall diversity, leaving microbial interaction dynamics largely unexplored. Correlation-based microbial networks offer an interpretable framework for studying such interactions but are challenging to estimate from compositional microbiome data. Although compositionality-aware methods such as CCLasso provide principled multivariate inference, we identify a previously overlooked limitation: sensitivity to random seeds, which leads to unstable correlation estimates and irreproducible significance assessments. Methods: To address this issue, we propose Robust CCLasso (rCCLasso), a statistically rigorous framework that stabilizes microbial correlation estimation by integrating CCLasso outputs across multiple runs. rCCLasso aggregates sparse correlation estimates using median-based integration with positive-definite projection and combines run-specific inference through the Cauchy combination test with an additional stability criterion to control type-I error. The method is naturally parallelizable and computationally scalable. Results: Simulation studies demonstrate that rCCLasso improves inferential stability, type-I error control, and power relative to the original CCLasso. Applying rCCLasso to data from over 4,000 healthy adults in the American Gut Project (ages 18--101), we uncover age-related microbial network dynamics, characterized by marked fluctuations from early to mid-adulthood, followed by a relatively stable phase and a substantial decline in network strength in the elderly group. Discussion: Together, these results establish rCCLasso as a robust and interpretable framework for studying microbial networks in aging research.