Tests for differential associations among features over ordered experimental groups

Thomas M. Menino Convention & Exhibition Center 

There is growing interest in understanding how interactions among features in complex systems evolve across experimental conditions. In many scientific studies, experimental groups are naturally ordered, such as disease stages, exposure levels, or time points. In such settings, it is scientifically meaningful to assess whether associations between pairs of variables evolve systematically across the ordering. To address this problem, we develop a class of ordered correlation procedures for second-order inference across multiple ordered environments. The proposed framework enables formal testing of whether correlations or partial correlations between pairs of features change systematically across ordered groups, as well as identification of features that drive coordinated changes in correlation networks. The methods accommodate high-dimensional settings, missing data, and both marginal and conditional measures of dependence. We evaluate finite-sample performance through simulations and illustrate practical utility using multi-omics data from the Multi-Center AIDS Cohort Study (MACS), providing a principled framework for analyzing evolving association networks in ordered multi-environment studies.

constrained statistical inference


differential correlation mining


ordered experimental groups

longitudinal studies