Evaluation of deep learning-based feature selection for single-cell RNA sequencing data analysis.

Journal: Genome biology
PMID: 37950331

Abstract

BACKGROUND: Feature selection is an essential task in single-cell RNA-seq (scRNA-seq) data analysis and can be critical for gene dimension reduction and downstream analyses, such as gene marker identification and cell type classification. Most popular methods for feature selection from scRNA-seq data are based on the concept of differential distribution wherein a statistical model is used to detect changes in gene expression among cell types. Recent development of deep learning-based feature selection methods provides an alternative approach compared to traditional differential distribution-based methods in that the importance of a gene is determined by neural networks.

Authors

  • Hao Huang
    School of Information Science and Engineering, Xinjiang University, Shangli Road, Urumqi 830046, 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.
  • Manoj M Wagle
    Computational Systems Biology Unit, Faculty of Medicine and Health, Children's Medical Research Institute, University of Sydney, Westmead, NSW, 2145, 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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