Department of Epidemiology and Biostatistics, University of California, 550 16th Street, San Francisco, CA 94143-0560, USA.
Biostatistics. 2023 Jul 14;24(3):618-634. doi: 10.1093/biostatistics/kxab033.
Three-dimensional (3D) genome architecture is critical for numerous cellular processes, including transcription, while certain conformation-driven structural alterations are frequently oncogenic. Inferring 3D chromatin configurations has been advanced by the emergence of chromatin conformation capture assays, notably Hi-C, and attendant 3D reconstruction algorithms. These have enhanced understanding of chromatin spatial organization and afforded numerous downstream biological insights. Until recently, comparisons of 3D reconstructions between conditions and/or cell types were limited to prescribed structural features. However, multiMDS, a pioneering approach developed by Rieber and Mahony (2019). that performs joint reconstruction and alignment, enables quantification of all locus-specific differences between paired Hi-C data sets. By subsequently mapping these differences to the linear (1D) genome the identification of relocalization regions is facilitated through the use of peak calling in conjunction with continuous wavelet transformation. Here, we seek to refine this approach by performing the search for significant relocalization regions in terms of the 3D structures themselves, thereby retaining the benefits of 3D reconstruction and avoiding limitations associated with the 1D perspective. The search for (extreme) relocalization regions is conducted using the patient rule induction method (PRIM). Considerations surrounding orienting structures with respect to compartmental and principal component axes are discussed, as are approaches to inference and reconstruction accuracy assessment. The illustration makes recourse to comparisons between four different cell types.
三维(3D)基因组结构对于包括转录在内的许多细胞过程至关重要,而某些构象驱动的结构改变通常是致癌的。染色质构象捕获测定法(尤其是 Hi-C)的出现以及伴随的 3D 重建算法推动了 3D 染色质构象的推断。这些方法增强了对染色质空间组织的理解,并提供了许多下游的生物学见解。直到最近,条件和/或细胞类型之间的 3D 重建比较还仅限于规定的结构特征。然而,由 Rieber 和 Mahony(2019 年)开发的开创性方法 multiMDS 执行联合重建和对齐,能够量化配对 Hi-C 数据集之间所有特定于位置的差异。随后,通过将这些差异映射到线性(1D)基因组,通过使用峰调用结合连续小波变换,有助于识别重新定位区域。在这里,我们试图通过根据 3D 结构本身来执行寻找重要的重新定位区域的方法来改进这种方法,从而保留 3D 重建的优势并避免与 1D 视角相关的限制。使用患者规则归纳法(PRIM)来搜索(极端)重新定位区域。讨论了围绕着隔室和主成分轴的定向结构的考虑因素,以及推理和重建准确性评估的方法。该说明借助于对四种不同细胞类型之间的比较。