Open AccessHigh-speed automatic characterization of rare events in flow cytometric data
Author(s) -
Yuan Qi,
Youhan Fang,
David R. Sinclair,
Shangqin Guo,
Meritxell AlberichJorda,
Jun Lü,
Daniel G. Tenen,
Michael G. Kharas,
Saumyadipta Pyne
Publication year - 2020
Publication title -
plos one
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.99
H-Index - 332
ISSN - 1932-6203
DOI - 10.1371/journal.pone.0228651
Subject(s) - computer science , flow cytometry , rare events , parametric statistics , computational biology , computation , focus (optics) , bayesian probability , biology , algorithm , artificial intelligence , physics , genetics , mathematics , statistics , optics
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.