scAuto as a comprehensive framework for single-cell chromatin accessibility data analysis (2024)

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Authors: Meiqin Gong, Yun Yu, Zixuan Wang, Junming Zhang, + 4, Xiongyi Wang, Cheng Fu, Yongqing Zhang, and Xiaodong Wang (Less)

Published: 09 July 2024 Publication History

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    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.

    Highlights

    Present a framework for single-cell chromatin accessibility analysis.

    Develop an online analysis platform, scAuto.

    Conduct extensive experiments and achieve the state-of-the-art performance.

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      Published In

      scAuto as a comprehensive framework for single-cell chromatin accessibility data analysis (1)

      Computers in Biology and Medicine Volume 171, Issue C

      Mar 2024

      1547 pages

      ISSN:0010-4825

      Issue’s Table of Contents

      Elsevier Ltd.

      Publisher

      Pergamon Press, Inc.

      United States

      Publication History

      Published: 09 July 2024

      Author Tags

      1. Single-cell genomics
      2. Chromatin accessibility
      3. Data analysis tools
      4. Web server
      5. Deep learning

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