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Related Experiment Videos

Tailor: Targeting heavy tails in flow cytometry data with fast, interpretable mixture modeling.

Matei Ionita1, Richard Schretzenmair1, Derek Jones1

  • 1Department of Pathology and Laboratory Medicine, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, USA.

Cytometry. Part a : the Journal of the International Society for Analytical Cytology
|January 21, 2021
PubMed
Summary

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Tailor, a new algorithm for flow cytometry data analysis, efficiently clusters millions of cells. It provides automated, interpretable results for high-parameter cytometry studies.

Area of Science:

  • Computational Biology
  • Immunology
  • Data Science

Background:

  • Automated clustering is crucial for analyzing high-parameter flow cytometry data.
  • Existing algorithms struggle with large datasets and interpretation.

Purpose of the Study:

  • To develop an efficient and interpretable clustering algorithm for flow cytometry data.
  • To address the need for robust analysis of large cell populations.

Main Methods:

  • Developed Tailor, a model-based clustering algorithm.
  • Utilized a phenotype-aware binning scheme and a multivariate Gaussian mixture model.
  • Benchmarked using simulations and real flow cytometry datasets.

Main Results:

  • Tailor processes millions of data points quickly and efficiently.
Keywords:
clustering algorithmscomputation and informaticshigh-parameter flow cytometrymixture modeling

Related Experiment Videos

  • Results are robust to deviations from normality and inter-sample variations.
  • Automated, non-overlapping cluster annotations facilitate interpretation.

    • Conclusions:

    • Tailor offers an effective solution for high-parameter flow cytometry data analysis.
    • The algorithm enhances interpretability and downstream analysis.
    • Tailor is available as an R package with publicly accessible source code.