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Deep generative modeling and clustering of single cell Hi-C data
Qiao Liu, Wanwen Zeng, Wei Zhang, Sicheng Wang, Hongyang Chen, Rui Jiang, Mu Zhou, Shaoting Zhang
doi: https://doi.org/10.1101/2022.07.19.500573
Qiao Liu
1Department of Statistics, Stanford University, Stanford, CA 94305, USA
Wanwen Zeng
2College of Software, Nankai University, Tianjin 300071, China
Wei Zhang
3Department of Biomedical Engineering, School of Control Science and Engineering, Shandong University, Jinan, Shandong 250061, China
Sicheng Wang
4Department of Computer Science and Engineering, University of California San Diego, La Jolla, CA 92093, USA
Hongyang Chen
5The Research Center for Intelligent Network, Zhejiang Lab, Hangzhou 311121, China
Rui Jiang
6Ministry of Education Key Laboratory of Bioinformatics, Research Department of Bioinformatics at the Beijing National Research Center for Information Science and Technology, Center for Synthetic and Systems Biology, Department of Automation, Tsinghua University, Beijing 100084, China
Mu Zhou
7SenseBrain Research, San Jose, CA 95131, USA
Shaoting Zhang
8Shanghai Artificial Intelligence Laboratory, Shanghai 200240, China
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- Supplementary Data[supplements/500573_file02.docx]
Posted July 20, 2022.
Deep generative modeling and clustering of single cell Hi-C data
Qiao Liu, Wanwen Zeng, Wei Zhang, Sicheng Wang, Hongyang Chen, Rui Jiang, Mu Zhou, Shaoting Zhang
bioRxiv 2022.07.19.500573; doi: https://doi.org/10.1101/2022.07.19.500573
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