fastACCORD enables ultrahigh-dimensional partial correlation modeling for multi-omic data integration

Abstract

Gene co-expression networks reflect transcription factor-associated regulation together with epigenetic influences such as DNA methylation, chromatin states, and histone modifications. To distinguish gene-gene dependencies that persist after accounting for methylation covariation, large-scale statistical inference conditioning on hundreds of thousands of molecular features is necessary, but the task remains computationally intractable for conventional Gaussian graphical modelling approaches. Here we present fastACCORD, a scalable computational framework for ultrahigh-dimensional partial correlation modeling. fastACCORD combines row-separable optimization, {ell}2 stabilization, and a semismooth Newton solver in a PyTorch implementation for CPU and CUDA-enabled GPU hardware. We applied it to matched transcriptomic and methylomic profiles from 16 TCGA cancer types and obtained joint networks containing >300,000 molecular features per cancer. The resulting multi-omic networks revealed cancer-specific methylation-expression dependencies, including recurrent methylation-associated expression repression of metabolic genes. Methylation-adjusted gene co-expression networks were sparser than networks estimated from mRNA data alone, but more enriched for ChIP-seq-supported TF-target relationships and curated co-regulon annotations. TF-target subnetworks further revealed cancer-specific architectures consistent with lineage identity, oncofetal reactivation, and tumor microenvironment-associated programs. Together, these analyses establish fastACCORD as a practical framework for ultrahigh-dimensional multi-omic partial correlation modeling and demonstrate how joint modeling of transcriptomic and epigenomic measurements can refine the interpretation of gene co-expression networks.