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Part 1: Document Description
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Citation |
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Title: |
Replication Data for: Persistent spectral simplicial complex-based machine learning for chromosomal structural analysis in cellular differentiation |
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Identification Number: |
doi:10.21979/N9/SBFIZD |
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Distributor: |
DR-NTU (Data) |
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Date of Distribution: |
2023-06-23 |
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Version: |
1 |
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Bibliographic Citation: |
Gong, Weikang; Wee, JunJie; Wu, Min-Chun; Sun, Xiaohan; Li, Chunhua; Xia, Kelin, 2023, "Replication Data for: Persistent spectral simplicial complex-based machine learning for chromosomal structural analysis in cellular differentiation", https://doi.org/10.21979/N9/SBFIZD, DR-NTU (Data), V1 |
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Citation |
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Title: |
Replication Data for: Persistent spectral simplicial complex-based machine learning for chromosomal structural analysis in cellular differentiation |
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Identification Number: |
doi:10.21979/N9/SBFIZD |
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Authoring Entity: |
Gong, Weikang (Beijing University of Technology) |
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Wee, JunJie (Nanyang Technological University) |
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Wu, Min-Chun (Nanyang Technological University) |
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Sun, Xiaohan (Beijing University of Technology) |
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Li, Chunhua (Beijing University of Technology) |
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Xia, Kelin (Nanyang Technological University) |
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Software used in Production: |
Python |
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Software used in Production: |
MATLAB |
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Grant Number: |
M4081842.110 |
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Grant Number: |
RG109/19 |
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Grant Number: |
MOE2018-T2-1-033 |
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Grant Number: |
MOE-T2EP20120-0013 |
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Grant Number: |
31971180 |
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Grant Number: |
201906540026 |
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Distributor: |
DR-NTU (Data) |
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Access Authority: |
Gong, Weikang |
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Access Authority: |
Li, Chunhua |
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Access Authority: |
Xia, Kelin |
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Depositor: |
Wee, JunJie |
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Date of Deposit: |
2023-06-23 |
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Holdings Information: |
https://doi.org/10.21979/N9/SBFIZD |
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Study Scope |
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Keywords: |
Mathematical Sciences, Medicine, Health and Life Sciences, Mathematical Sciences, Medicine, Health and Life Sciences, Hi-C data, Hodge Laplacian, persistent spectral simplicial complex, chromosomal featurization, machine learning |
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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. |
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Kind of Data: |
Calculated data and codes |
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Methodology and Processing |
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Sources Statement |
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Data Access |
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Other Study Description Materials |
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Related Publications |
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Citation |
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Identification Number: |
10.1093/bib/bbac168 |
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Bibliographic Citation: |
Gong, W., Wee, J., Wu, M. C., 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. |
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Citation |
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Identification Number: |
10356/168972 |
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Bibliographic Citation: |
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- |
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Label: |
PerSpectSC-ML.rar |
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Notes: |
application/x-rar-compressed |