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conST: an interpretable multi-modal contrastive learning framework for spatial transcriptomics
Yongshuo Zong, Tingyang Yu, Xuesong Wang, View ORCID ProfileYixuan Wang, Zhihang Hu, View ORCID ProfileYu Li
doi: https://doi.org/10.1101/2022.01.14.476408
Yongshuo Zong
1School of Informatics, the University of Edinburgh, Edinburgh, United Kingdom
Tingyang Yu
2Department of Computer Science and Engineering, Chinese University of Hong Kong, Hong Kong SAR, China
3Department of Mathematics, Chinese University of Hong Kong, Hong Kong SAR, China
4Department of Information Engineering, Chinese University of Hong Kong, Hong Kong SAR, China
Xuesong Wang
2Department of Computer Science and Engineering, Chinese University of Hong Kong, Hong Kong SAR, China
Yixuan Wang
2Department of Computer Science and Engineering, Chinese University of Hong Kong, Hong Kong SAR, China
5Department of Mathematics, Harbin Institute of Technology, Weihai, 264209, China
Zhihang Hu
2Department of Computer Science and Engineering, Chinese University of Hong Kong, Hong Kong SAR, China
Yu Li
2Department of Computer Science and Engineering, Chinese University of Hong Kong, Hong Kong SAR, China
6The CUHK Shenzhen Research Institute, Hi-Tech Park, Nanshan, Shenzhen, 518057, China
Posted January 17, 2022.
conST: an interpretable multi-modal contrastive learning framework for spatial transcriptomics
Yongshuo Zong, Tingyang Yu, Xuesong Wang, Yixuan Wang, Zhihang Hu, Yu Li
bioRxiv 2022.01.14.476408; doi: https://doi.org/10.1101/2022.01.14.476408
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