Deep Learning in Single-Cell Analysis

IF 7.2 4区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Dylan Molho, Jiayuan Ding, Wenzhuo Tang, Zhaoheng Li, Hongzhi Wen, Yixin Wang, Julian Venegas, Wei Jin, Renming Liu, Runze Su, Patrick Danaher, Robert Yang, Yu Leo Lei, Yuying Xie, Jiliang Tang
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引用次数: 0

Abstract

Single-cell technologies are revolutionizing the entire field of biology. The large volumes of data generated by single-cell technologies are high-dimensional, sparse, heterogeneous, and have complicated dependency structures, making analyses using conventional machine learning approaches challenging and impractical. In tackling these challenges, deep learning often demonstrates superior performance compared to traditional machine learning methods. In this work, we give a comprehensive survey on deep learning in single-cell analysis. We first introduce background on single-cell technologies and their development, as well as fundamental concepts of deep learning including the most popular deep architectures. We present an overview of the single-cell analytic pipeline pursued in research applications while noting divergences due to data sources or specific applications. We then review seven popular tasks spanning through different stages of the single-cell analysis pipeline, including multimodal integration, imputation, clustering, spatial domain identification, cell-type deconvolution, cell segmentation, and cell-type annotation. Under each task, we describe the most recent developments in classical and deep learning methods and discuss their advantages and disadvantages. Deep learning tools and benchmark datasets are also summarized for each task. Finally, we discuss the future directions and the most recent challenges. This survey will serve as a reference for biologists and computer scientists, encouraging collaborations.

单细胞分析中的深度学习
单细胞技术正在彻底改变整个生物学领域。单细胞技术产生的大量数据具有高维、稀疏、异构和复杂的依赖结构,使得使用传统机器学习方法进行分析变得具有挑战性和不切实际。在应对这些挑战时,深度学习往往比传统机器学习方法表现出更优越的性能。在这项工作中,我们对深度学习在单细胞分析中的应用进行了全面研究。我们首先介绍了单细胞技术及其发展的背景,以及深度学习的基本概念,包括最流行的深度架构。我们概述了研究应用中采用的单细胞分析流水线,同时指出了因数据源或特定应用而产生的差异。然后,我们回顾了横跨单细胞分析管道不同阶段的七项流行任务,包括多模态整合、估算、聚类、空间域识别、细胞类型解卷积、细胞分割和细胞类型注释。在每项任务下,我们都介绍了经典和深度学习方法的最新发展,并讨论了它们的优缺点。我们还总结了每项任务的深度学习工具和基准数据集。最后,我们讨论了未来的方向和最新的挑战。这份调查报告将为生物学家和计算机科学家提供参考,鼓励他们开展合作。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
ACM Transactions on Intelligent Systems and Technology
ACM Transactions on Intelligent Systems and Technology COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE-COMPUTER SCIENCE, INFORMATION SYSTEMS
CiteScore
9.30
自引率
2.00%
发文量
131
期刊介绍: ACM Transactions on Intelligent Systems and Technology is a scholarly journal that publishes the highest quality papers on intelligent systems, applicable algorithms and technology with a multi-disciplinary perspective. An intelligent system is one that uses artificial intelligence (AI) techniques to offer important services (e.g., as a component of a larger system) to allow integrated systems to perceive, reason, learn, and act intelligently in the real world. ACM TIST is published quarterly (six issues a year). Each issue has 8-11 regular papers, with around 20 published journal pages or 10,000 words per paper. Additional references, proofs, graphs or detailed experiment results can be submitted as a separate appendix, while excessively lengthy papers will be rejected automatically. Authors can include online-only appendices for additional content of their published papers and are encouraged to share their code and/or data with other readers.
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