LAWS-HiC: A Locally Adaptive Weighting and Screening (LAWS) Approach to Improve Detection of Long-Range Chromatin Interactions from Hi-C Data.

Hi-C technologies are widely used to study genome-wide chromosome spatial organization. Among various Hi-C data analyses, detecting long-range chromatin interactions (i.e., 3D peak calling) is particularly critical due to its direct relevance to gene regulation. However, most existing peak callers fail to account for spatial dependencies in high-resolution (e.g., ≤10 kb) Hi-C data, often resulting in suboptimal accuracy. To address this limitation, we introduce LAWS-HiC, a novel computational method based on the Locally Adaptive Weighting and Screening (LAWS) approach. LAWS-HiC adjusts p-values from a standard Hi-C peak caller by incorporating local spatial dependence within topologically associating domains (TADs) in a data-driven manner. Benchmarking in two deeply sequenced Hi-C datasets from human lymphoblastoid cell line GM12878 and mouse embryonic stem cells (mESCs), LAWS-HiC consistently improves the area under the precision-recall curve (PRAUC) across varying sequencing depths, with larger gains at lower sequencing depths. At the standard Benjamini-Hochberg false discovery rate (BH-FDR) ≤ 0.05, biological feature overlap analysis confirms that LAWS-HiC calls were more strongly enriched for regulatory annotations relative to non-calls than those from alternative methods. LAWS-HiC is freely available as an R package on GitHub.