Please use this identifier to cite or link to this item:
Title: Persistent spectral simplicial complex-based machine learning for chromosomal structural analysis in cellular differentiation
Authors: Gong, Weikang
Wee, Junjie
Wu, Min-Chun
Sun, Xiaohan
Li, Chunhua
Xia, Kelin
Keywords: Science::Mathematics
Issue Date: 2022
Source: Gong, W., Wee, J., Wu, M., Sun, X., Li, C. & Xia, K. (2022). Persistent spectral simplicial complex-based machine learning for chromosomal structural analysis in cellular differentiation. Briefings in Bioinformatics, 23(4), bbac168-.
Project: M4081842.110
Journal: Briefings in Bioinformatics
Abstract: 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.
ISSN: 1467-5463
DOI: 10.1093/bib/bbac168
DOI (Related Dataset): 10.21979/N9/SBFIZD
Schools: School of Physical and Mathematical Sciences 
Rights: © 2022 The Author(s). Published by Oxford University Press. All rights reserved.
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:SPMS Journal Articles

Citations 50

Updated on Jun 19, 2024

Web of ScienceTM
Citations 50

Updated on Oct 27, 2023

Page view(s)

Updated on Jun 22, 2024

Google ScholarTM




Items in DR-NTU are protected by copyright, with all rights reserved, unless otherwise indicated.