Persistent spectral simplicial complex-based machine learning for chromosomal structural analysis in cellular differentiation

被引:7
作者
Gong, Weikang [1 ,2 ]
Wee, JunJie [2 ]
Wu, Min-Chun [2 ]
Sun, Xiaohan [1 ]
Li, Chunhua [1 ]
Xia, Kelin [2 ]
机构
[1] Beijing Univ Technol, Fac Environm & Life Sci, Beijing 100124, Peoples R China
[2] Nanyang Technol Univ, Sch Phys & Math Sci, Div Math Sci, Singapore 637371, Singapore
基金
中国国家自然科学基金;
关键词
Hi-C data; Hodge Laplacian; persistent spectral simplicial complex; chromosomal featurization; machine learning; ELASTIC NETWORK MODEL; 3D GENOME; DYNAMICS; DOMAINS;
D O I
10.1093/bib/bbac168
中图分类号
Q5 [生物化学];
学科分类号
071010 ; 081704 ;
摘要
The three-dimensional (3D) chromosomal structure plays an essential role in all DNA-templated processes, including gene transcription, DNA replication and other cellular processes. Although developing chromosome conformation capture (3C) methods, such as Hi-C, which can generate chromosomal contact data characterized genome-wide chromosomal structural properties, understanding 3D genomic nature-based on Hi-C data remains lacking. Here, we propose a persistent spectral simplicial complex (PerSpectSC) model to describe Hi-C data for the first time. Specifically, a filtration process is introduced to generate a series of nested simplicial complexes at different scales. For each of these simplicial complexes, its spectral information can be calculated from the corresponding Hodge Laplacian matrix. PerSpectSC model describes the persistence and variation of the spectral information of the nested simplicial complexes during the filtration process. Different from all previous models, our PerSpectSC-based features provide a quantitative global-scale characterization of chromosome structures and topology. Our descriptors can successfully classify cell types and also cellular differentiation stages for all the 24 types of chromosomes simultaneously. In particular, persistent minimum best characterizes cell types and Dim (1) persistent multiplicity best characterizes cellular differentiation. These results demonstrate the great potential of our PerSpectSC-based models in polymeric data analysis.
引用
收藏
页数:11
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