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Gong M, Yu Y, Wang Z, Zhang J, Wang X, Fu C, Zhang Y, Wang X. scAuto as a comprehensive framework for single-cell chromatin accessibility data analysis. Comput Biol Med 2024; 171:108230. [PMID: 38442554 DOI: 10.1016/j.compbiomed.2024.108230] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2023] [Revised: 02/06/2024] [Accepted: 02/25/2024] [Indexed: 03/07/2024]
Abstract
Interpreting single-cell chromatin accessibility data is crucial for understanding intercellular heterogeneity regulation. Despite the progress in computational methods for analyzing this data, there is still a lack of a comprehensive analytical framework and a user-friendly online analysis tool. To fill this gap, we developed a pre-trained deep learning-based framework, single-cell auto-correlation transformers (scAuto), to overcome the challenge. Following DNABERT's methodology of pre-training and fine-tuning, scAuto learns a general understanding of DNA sequence's grammar by being pre-trained on unlabeled human genome via self-supervision; it is then transferred to the single-cell chromatin accessibility analysis task of scATAC-seq data for supervised fine-tuning. We extensively validated scAuto on the Buenrostro2018 dataset, demonstrating its superior performance on chromatin accessibility prediction, single-cell clustering, and data denoising. Based on scAuto, we further developed an interactive web server for single-cell chromatin accessibility data analysis. It integrates tutorial-style interfaces for those with limited programming skills. The platform is accessible at http://zhanglab.icaup.cn. To our knowledge, this work is expected to help analyze single-cell chromatin accessibility data and facilitate the development of precision medicine.
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Affiliation(s)
- Meiqin Gong
- Department of Obstetrics and Gynecology, West China Second University Hospital, Sichuan University, Chengdu, 610041, China
| | - Yun Yu
- School of Computer Science, Chengdu University of Information Technology, Chengdu, 610225, China
| | - Zixuan Wang
- College of Electronics and information Engineering, SiChuan University, Chengdu, 610065, China
| | - Junming Zhang
- School of Computer Science, Chengdu University of Information Technology, Chengdu, 610225, China
| | - Xiongyi Wang
- School of Computer Science, Chengdu University of Information Technology, Chengdu, 610225, China
| | - Cheng Fu
- School of Computer Science, Chengdu University of Information Technology, Chengdu, 610225, China
| | - Yongqing Zhang
- School of Computer Science, Chengdu University of Information Technology, Chengdu, 610225, China
| | - Xiaodong Wang
- Department of Obstetrics and Gynecology, West China Second University Hospital, Sichuan University, Chengdu, 610041, China.
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