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.

Cancer Science Institute National University of Singapore Singapore Singapore

Center for Life Sciences Harvard Medical School Boston MA United States of America

Department of Biostatistics Graduate School of Public Health University of Pittsburgh Pittsburgh PA United States of America

Department of Cell Biology Yale University School of Medicine New Haven CT United States of America

Department of Computer Science Purdue University West Lafayette IN United States of America

Department of Genetics Yale University School of Medicine New Haven CT United States of America

Department of Health Policy and Management Graduate School of Public Health University of Pittsburgh Pittsburgh PA United States of America

Department of Statistics Purdue University West Lafayette IN United States of America

Harvard Stem Cell Institute Harvard Medical School Boston MA United States of America

Institute of Molecular Genetics of the ASCR Prague Czech Republic

Molecular Pharmacology Program Memorial Sloan Kettering Cancer Center New York NY United States of America

Population Health Sciences Institute Newcastle University Newcastle upon Tyne United Kingdom

Public Health Dynamics Laboratory Graduate School of Public Health University of Pittsburgh Pittsburgh PA United States of America

Yale Stem Cell Center Yale University School of Medicine New Haven CT United States of America

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