Ensemble deep learning of embeddings for clustering multimodal single-cell omics data.

Journal: Bioinformatics (Oxford, England)
PMID: 37314966

Abstract

MOTIVATION: Recent advances in multimodal single-cell omics technologies enable multiple modalities of molecular attributes, such as gene expression, chromatin accessibility, and protein abundance, to be profiled simultaneously at a global level in individual cells. While the increasing availability of multiple data modalities is expected to provide a more accurate clustering and characterization of cells, the development of computational methods that are capable of extracting information embedded across data modalities is still in its infancy.

Authors

  • Lijia Yu
    National Center for Occupational Safety and Health, Beijing 102308, China.
  • 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.
  • Jean Yee Hwa Yang
    Centre for Mathematical Biology, School of Mathematics and Statistics, University of Sydney, Sydney, Australia.
  • 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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