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

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...

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Main Authors: Gong, Weikang, Wee, Junjie, Wu, Min-Chun, Sun, Xiaohan, Li, Chunhua, Xia, Kelin
Other Authors: School of Physical and Mathematical Sciences
Format: Article
Language:English
Published: 2023
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Online Access:https://hdl.handle.net/10356/168972
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-1689722023-06-27T02:07:13Z Persistent spectral simplicial complex-based machine learning for chromosomal structural analysis in cellular differentiation Gong, Weikang Wee, Junjie Wu, Min-Chun Sun, Xiaohan Li, Chunhua Xia, Kelin School of Physical and Mathematical Sciences Science::Mathematics Hi-C Data Hodge Laplacian Persistent Spectral Simplicial Complex Chromosomal Featurization Machine Learning 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. Ministry of Education (MOE) Nanyang Technological University Nanyang Technological University Startup Grant (grant no. M4081842.110); Singapore Ministry of Education Academic Research fund (grant nos. Tier 1 RG109/19, Tier 2 MOE2018-T2-1-033, Tier 2 MOE-T2EP20120-0013); National Natural Science Foundation of China (grant no. 31971180); China Scholarship Council (grant no. 201906540026). 2023-06-27T02:07:13Z 2023-06-27T02:07:13Z 2022 Journal Article 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-. https://dx.doi.org/10.1093/bib/bbac168 1467-5463 https://hdl.handle.net/10356/168972 10.1093/bib/bbac168 23 2-s2.0-85134721465 4 23 bbac168 en M4081842.110 RG109/19 MOE2018-T2-1-033 MOE-T2EP20120-0013 Briefings in Bioinformatics 10.21979/N9/SBFIZD © 2022 The Author(s). Published by Oxford University Press. All rights reserved.
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic Science::Mathematics
Hi-C Data
Hodge Laplacian
Persistent Spectral Simplicial Complex
Chromosomal Featurization
Machine Learning
spellingShingle Science::Mathematics
Hi-C Data
Hodge Laplacian
Persistent Spectral Simplicial Complex
Chromosomal Featurization
Machine Learning
Gong, Weikang
Wee, Junjie
Wu, Min-Chun
Sun, Xiaohan
Li, Chunhua
Xia, Kelin
Persistent spectral simplicial complex-based machine learning for chromosomal structural analysis in cellular differentiation
description 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.
author2 School of Physical and Mathematical Sciences
author_facet School of Physical and Mathematical Sciences
Gong, Weikang
Wee, Junjie
Wu, Min-Chun
Sun, Xiaohan
Li, Chunhua
Xia, Kelin
format Article
author Gong, Weikang
Wee, Junjie
Wu, Min-Chun
Sun, Xiaohan
Li, Chunhua
Xia, Kelin
author_sort Gong, Weikang
title Persistent spectral simplicial complex-based machine learning for chromosomal structural analysis in cellular differentiation
title_short Persistent spectral simplicial complex-based machine learning for chromosomal structural analysis in cellular differentiation
title_full Persistent spectral simplicial complex-based machine learning for chromosomal structural analysis in cellular differentiation
title_fullStr Persistent spectral simplicial complex-based machine learning for chromosomal structural analysis in cellular differentiation
title_full_unstemmed Persistent spectral simplicial complex-based machine learning for chromosomal structural analysis in cellular differentiation
title_sort persistent spectral simplicial complex-based machine learning for chromosomal structural analysis in cellular differentiation
publishDate 2023
url https://hdl.handle.net/10356/168972
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