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Cytometry masked autoencoder: An accurate and interpretable automated immunophenotyper.

Jaesik Kim1, Matei Ionita2, Matthew Lee3

  • 1Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, USA; Institute for Immunology & Immune Health (I3H), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; Institute for Biomedical Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA.

Cell Reports. Medicine
|November 8, 2024
PubMed
Summary

We developed a cytometry masked autoencoder (cyMAE) to automate cell type annotation in cytometry data. This AI model enhances accuracy and interpretability for large-scale immunology studies.

Keywords:
automated gatingdeep learninghigh-dimensional cytometryimmunophenotypingmachine learningmass cytometryrepresentation learning

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Area of Science:

  • Immunology
  • Computational Biology
  • Bioinformatics

Background:

  • Single-cell cytometry is vital for immune system research.
  • Current cell annotation methods struggle with scalability, robustness, and accuracy.

Purpose of the Study:

  • To automate immunophenotyping and cell type annotation using a novel approach.
  • To improve the interpretability and cross-study comparability of cytometry data analysis.

Main Methods:

  • Developed a cytometry masked autoencoder (cyMAE) model.
  • Employed a two-phase training: self-supervised learning on unlabeled data, followed by fine-tuning on annotated data.
  • Validated the model across multiple studies using the same cytometry panel.

Main Results:

  • cyMAE accurately and interpretably annotates cell types in cytometry data.
  • The model improves the prediction of subject-level metadata.
  • Training cost is amortized over repeated inferences on new datasets.

Conclusions:

  • cyMAE offers a scalable and robust solution for automated immunophenotyping.
  • This approach represents a significant advancement for large-scale immunology research.
  • The model's adherence to user-defined cell types enhances interpretability.