Comparing Statistical Metrics for Differential Network Analysis

C. Ruiz de Porras Rubio, A. Sánchez Pla, C. Andrés-Lacueva, D. Garrido-Martín, A. Miñarro Alonso

Network analysis is a powerful tool in biology, medicine, and social sciences for studying complex systems, with nodes representing entities and edges their interactions. In this work, networks are constructed from partial correlation matrices, where edges represent associations between pairs of variables conditional on all others. Changes in networks help reveal pathological mechanisms and system responses to external stimuli. Differential network analysis compares structures under different conditions, detecting shifts in connections beyond individual node expression. This study evaluates statistical metrics on simulated weighted matrices using permutation tests, showing that Frobenius distance, Invariant Network Structure, L1-norm and Correlation distance effectively detect subtle structural differences. Applied to real data, the approach identifies significant changes between experimental conditions.

Palabras clave: Partial correlation networks Differential network analysis Permutation tests Distance metrics Weighted matrices Power analysis

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