Biostatistics. 2026 Jan 20;27(1):kxag021. doi: 10.1093/biostatistics/kxag021.
ABSTRACT
The three dimensional (3D) spatial organization of the genome is closely linked to biological functions and can be captured by Hi-C assays through interrogating genome-wide chromatin interactions. Methodologies for inferring 3D structures from Hi-C data summarized as a two-dimensional (2D) contact matrix can be broadly placed within the paradigms of optimization-based and sampling-based. Many optimization-based methods are capable of constructing whole genome 3D structures but do not account for spatial dependency in the 2D data matrix nor cell heterogeneity in bulk Hi-C data, which provide an average over millions of cells. Sampling-based methods, on the other hand, are probabilistic model-based and can account for not only dependency, heterogeneity, but also other features inherent in Hi-C data, such as over-dispersion and sparsity. However, whole-genome 3D structure recapitulation is too computationally expensive for sampling-based methods, while chromosome-by-chromosome strategies for sampling-based methods ignore important information on inter-chromosomal contacts. To address these issues, we propose the truncated Random effect EXpression-cut and paste (tREX-cap) method, which applies the tREX model within a divide and conquer strategy. The resulting method inherits the good data-feature-cognizant properties of tREX and, in the meantime, can efficiently infer the whole genome 3D structure. We demonstrate the performance of tREX-cap through an extensive simulation study and analyses of a Hi-C lymphoblastoid dataset and a Hi-C IMR90 dataset.
PMID:42470130 | DOI:10.1093/biostatistics/kxag021
