CyAnno: a semi-automated approach for cell type annotation of mass cytometry datasets.

Journal: Bioinformatics (Oxford, England)
Published Date: Nov 18, 2021

Abstract

MOTIVATION: For immune system monitoring in large-scale studies at the single-cell resolution using CyTOF, (semi-)automated computational methods are applied for annotating live cells of mixed cell types. Here, we show that the live cell pool can be highly enriched with undefined heterogeneous cells, i.e. 'ungated' cells, and that current semi-automated approaches ignore their modeling resulting in misclassified annotations.

Authors

  • Abhinav Kaushik
    Department of Medicine, Sean N. Parker Center for Allergy and Asthma Research, Stanford University School of Medicine, Stanford, Calif; Department of Environmental Health, T. H. Chan School of Public Health, Harvard University, Boston, Mass.
  • Diane Dunham
    Department of Medicine, Sean N Parker Center for Allergy and Asthma Research at Stanford University, Stanford University, Stanford, CA 94305-5101, USA.
  • Ziyuan He
    Department of Medicine, Sean N Parker Center for Allergy and Asthma Research at Stanford University, Stanford University, Stanford, CA 94305-5101, USA.
  • Monali Manohar
    Department of Medicine, Sean N. Parker Center for Allergy and Asthma Research, Stanford University School of Medicine, Stanford, Calif.
  • Manisha Desai
    Quantitative Science Unit, Department of Medicine, Stanford University School of Medicine, Stanford, Calif.
  • Kari C Nadeau
    Department of Medicine, Sean N Parker Center for Allergy and Asthma Research at Stanford University, Stanford University, Stanford, CA 94305-5101, USA.
  • Sandra Andorf
    Department of Medicine, Sean N Parker Center for Allergy and Asthma Research at Stanford University, Stanford University, Stanford, CA 94305-5101, USA.

Keywords

External Resources

Popular Topics
Recent Journals