A0968
Title: Learning massive-scale partial correlation networks in clinical multi-omics studies with HP-ACCORD
Authors: Joong-Ho Won - Seoul National University (Korea, South) [presenting]
Sang-Yun Oh - University of California, Santa Barbara (United States)
Abstract: Graphical model estimation from modern multi-omics data requires a balance between statistical estimation performance and computational scalability. A novel pseudolikelihood-based graphical model framework is introduced that reparameterizes the target precision matrix while preserving the sparsity pattern and estimates it by minimizing an L1-penalized empirical risk based on a new loss function. The proposed estimator maintains estimation and selection consistency in various metrics under high-dimensional assumptions. The associated optimization problem allows for a provably fast computation algorithm using a novel operator-splitting approach and communication-avoiding distributed matrix multiplication. A high-performance computing implementation of the framework was tested in simulated data with up to one million variables, demonstrating complex dependency structures akin to biological networks. Leveraging this scalability, a partial correlation network is estimated from a dual-omic liver cancer data set. The co-expression network estimated from the ultrahigh-dimensional data showed superior specificity in prioritizing key transcription factors and co-activators by excluding the impact of epigenomic regulation, demonstrating the value of computational scalability in multi-omic data analysis.
