Examining dynamics of three-dimensional genome organization with multi-task matrix factorization

Three-dimensional (3D) genome organization, which determines how the DNA is packaged inside the nucleus, has emerged as a key component of the gene regulation machinery. High-throughput chromosome conformation datasets, such as Hi-C, have become available across multiple conditions and timepoints, offering a unique opportunity to examine changes in 3D genome organization and link them to phenotypic changes in normal and diseases processes. However, systematic detection of higher-order structural changes across multiple Hi-C datasets remains a major challenge. Existing computational methods either do not model higher-order structural units or cannot model dynamics across more than two conditions of interest. We address these limitations with Tree-Guided Integrated Factorization (TGIF), a generalizable multi-task Non-negative Matrix Factorization (NMF) approach that can be applied to time series or hierarchically related biological conditions. TGIF can identify large-scale changes at compartment or subcompartment levels, as well as local changes at boundaries of topologically associated domains (TADs). Compared to existing methods, TGIF boundaries are more enriched in CTCF and reproducible across biological replicates, normalization methods, depths, and resolutions. Application to three multi-sample mammalian datasets shows TGIF can detect differential regions at compartment, subcompartment, and boundary levels that are associated with significant changes in regulatory signals and gene expression enriched in tissue-specific processes. Finally, we leverage TGIF boundaries to prioritize sequence variants for multiple phenotypes from the NHGRI GWAS catalog. Taken together, TGIF is a flexible tool to examine 3D genome organization dynamics across disease and developmental processes.