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High-speed automatic characterization of rare events in flow cytometric data

Author

Listed:
  • Yuan Qi
  • Youhan Fang
  • David R Sinclair
  • Shangqin Guo
  • Meritxell Alberich-Jorda
  • Jun Lu
  • Daniel G Tenen
  • Michael G Kharas
  • Saumyadipta Pyne

Abstract

A new computational framework for FLow cytometric Analysis of Rare Events (FLARE) has been developed specifically for fast and automatic identification of rare cell populations in very large samples generated by platforms like multi-parametric flow cytometry. Using a hierarchical Bayesian model and information-sharing via parallel computation, FLARE rapidly explores the high-dimensional marker-space to detect highly rare populations that are consistent across multiple samples. Further it can focus within specified regions of interest in marker-space to detect subpopulations with desired precision.

Suggested Citation

  • Yuan Qi & Youhan Fang & David R Sinclair & Shangqin Guo & Meritxell Alberich-Jorda & Jun Lu & Daniel G Tenen & Michael G Kharas & Saumyadipta Pyne, 2020. "High-speed automatic characterization of rare events in flow cytometric data," PLOS ONE, Public Library of Science, vol. 15(2), pages 1-18, February.
    • Handle: RePEc:plo:pone00:0228651
    DOI: 10.1371/journal.pone.0228651
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    References listed on IDEAS

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    1. Andrew Cron & Cécile Gouttefangeas & Jacob Frelinger & Lin Lin & Satwinder K Singh & Cedrik M Britten & Marij J P Welters & Sjoerd H van der Burg & Mike West & Cliburn Chan, 2013. "Hierarchical Modeling for Rare Event Detection and Cell Subset Alignment across Flow Cytometry Samples," PLOS Computational Biology, Public Library of Science, vol. 9(7), pages 1-14, July.
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