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survey

Deep Learning in Single-cell Analysis

Published: 29 March 2024 Publication History

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, 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.

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  1. Deep Learning in Single-cell Analysis

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    cover image ACM Transactions on Intelligent Systems and Technology
    ACM Transactions on Intelligent Systems and Technology  Volume 15, Issue 3
    June 2024
    646 pages
    EISSN:2157-6912
    DOI:10.1145/3613609
    • Editor:
    • Huan Liu
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    Publication History

    Published: 29 March 2024
    Online AM: 26 January 2024
    Accepted: 29 December 2023
    Revised: 14 November 2023
    Received: 20 March 2023
    Published in TIST Volume 15, Issue 3

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    1. Deep learning
    2. single-cell Analysis
    3. multimodal integration
    4. imputation
    5. clustering
    6. spatial domain identification
    7. cell-type deconvolution
    8. cell segmentation
    9. cell-type annotation

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    • National Science Foundation (NSF)
    • National Institutes of Health (NIH)
    • Army Research Office (ARO)
    • Home Depot; Cisco Systems Inc.; Amazon Faculty Award; Johnson & Johnson; the JP Morgan Faculty Award; and SNAP

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