Department of Statistics, University of California Riverside, Riverside, CA 92521, USA.
Genomics Proteomics Bioinformatics. 2024 Jul 3;22(2). doi: 10.1093/gpbjnl/qzae028.
Recent advances in high-throughput chromosome conformation capture (Hi-C) techniques have allowed us to map genome-wide chromatin interactions and uncover higher-order chromatin structures, thereby shedding light on the principles of genome architecture and functions. However, statistical methods for detecting changes in large-scale chromatin organization such as topologically associating domains (TADs) are still lacking. Here, we proposed a new statistical method, DiffGR, for detecting differentially interacting genomic regions at the TAD level between Hi-C contact maps. We utilized the stratum-adjusted correlation coefficient to measure similarity of local TAD regions. We then developed a nonparametric approach to identify statistically significant changes of genomic interacting regions. Through simulation studies, we demonstrated that DiffGR can robustly and effectively discover differential genomic regions under various conditions. Furthermore, we successfully revealed cell type-specific changes in genomic interacting regions in both human and mouse Hi-C datasets, and illustrated that DiffGR yielded consistent and advantageous results compared with state-of-the-art differential TAD detection methods. The DiffGR R package is published under the GNU General Public License (GPL) ≥ 2 license and is publicly available at https://github.com/wmalab/DiffGR.
近年来，高通量染色体构象捕获（Hi-C）技术的进展使我们能够绘制全基因组染色质相互作用图谱，并揭示高级染色质结构，从而阐明基因组结构和功能的原理。然而，用于检测大规模染色质组织如拓扑关联域（TAD）变化的统计方法仍然缺乏。在这里，我们提出了一种新的统计方法 DiffGR，用于检测 Hi-C 接触图谱中 TAD 水平上差异相互作用的基因组区域。我们利用分层调整相关系数来测量局部 TAD 区域的相似性。然后，我们开发了一种非参数方法来识别基因组相互作用区域的统计学显著变化。通过模拟研究，我们证明了 DiffGR 可以在各种条件下稳健有效地发现差异基因组区域。此外，我们成功地揭示了人类和小鼠 Hi-C 数据集中原核生物相互作用区域的细胞类型特异性变化，并说明了与最先进的差异 TAD 检测方法相比，DiffGR 产生了一致和有利的结果。DiffGR R 包根据 GNU 通用公共许可证（GPL）≥2 版发布，并可在 https://github.com/wmalab/DiffGR 上公开获取。
