Single-cell technologies are revolutionizing the entire field of biology. The large volumes of data generated by single-cell technologies are high dimensional, sparse, and 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 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.

单细胞技术正在彻底改变整个生物学领域。单细胞技术生成的大量数据具有高维、稀疏、异构性，并且具有复杂的依赖结构，使得使用传统机器学习方法进行分析具有挑战性且不切实际。在应对这些挑战时，深度学习通常表现出比传统机器学习方法更优越的性能。在这项工作中，我们对单细胞分析中的深度学习进行了全面的调查。我们首先介绍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追求的单细胞分析流程，同时注意到由于数据源或特定应用而导致的差异。然后，我们回顾了跨越单细胞分析流程不同阶段的七个流行任务，包括多模态集成、插补、聚类、空间域识别、细胞类型反卷积、细胞分割和细胞类型注释。在每项任务下，我们描述经典和深度学习方法的最新发展，并讨论它们的优点和缺点。还针对每个任务总结了深度学习工具和基准数据集。最后，我们讨论未来的方向和最近的挑战。这项调查将为生物学家和计算机科学家提供参考，鼓励合作。