Interpretable deep learning in single-cell omics.

Journal: Bioinformatics (Oxford, England)
Published Date: Jun 3, 2024

Abstract

MOTIVATION: Single-cell omics technologies have enabled the quantification of molecular profiles in individual cells at an unparalleled resolution. Deep learning, a rapidly evolving sub-field of machine learning, has instilled a significant interest in single-cell omics research due to its remarkable success in analysing heterogeneous high-dimensional single-cell omics data. Nevertheless, the inherent multi-layer nonlinear architecture of deep learning models often makes them 'black boxes' as the reasoning behind predictions is often unknown and not transparent to the user. This has stimulated an increasing body of research for addressing the lack of interpretability in deep learning models, especially in single-cell omics data analyses, where the identification and understanding of molecular regulators are crucial for interpreting model predictions and directing downstream experimental validations.

Authors

  • Manoj M Wagle
    Computational Systems Biology Unit, Faculty of Medicine and Health, Children's Medical Research Institute, University of Sydney, Westmead, NSW, 2145, Australia.
  • Siqu Long
    Computational Systems Biology Unit, Children's Medical Research Institute, Faculty of Medicine and Health, The University of Sydney, Westmead, NSW 2145, Australia.
  • Carissa Chen
    Computational Systems Biology Unit, Children's Medical Research Institute, Faculty of Medicine and Health, The University of Sydney, Westmead, NSW 2145, Australia.
  • Chunlei Liu
    Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, CA, USA.; Helen Wills Neuroscience Institute, University of California, Berkeley, CA, USA.
  • Pengyi Yang
    Epigenetics & Stem Cell Biology Laboratory, National Institute of Environmental Health Sciences, National Institutes of Health, Research Triangle Park, Durham, North Carolina, United States of America; Biostatistics Branch, National Institute of Environmental Health Sciences, National Institutes of Health, Research Triangle Park, Durham, North Carolina, United States of America.

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