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Activity Number: 527
Type: Topic Contributed
Date/Time: Wednesday, August 12, 2015 : 10:30 AM to 12:20 PM
Sponsor: Section on Nonparametric Statistics
Abstract #317322
Title: Nonparametric Flow Cytometric Classifiers
Author(s): Ollivier Hyrien* and Andrea Baran and Michael Becker
Companies: University of Rochester and University of Rochester and University of Rochester
Keywords: kernel density estimation ; likelihood ratio ; multivariate data
Abstract:

Flow cytometry is a powerful experimental technique that permits the quantification of cellular properties using fluorescently labeled antibodies. It is routinely used in clinical settings for the diagnosis and prognosis of diseases through immunophenotyping. In this talk, we present a nonparametric approach for developing classifiers based on multidimensional flow cytometry data. Advantages and performance of the approach are demonstrated via simulations and applications to real data.

Authors who are presenting talks have a * after their name.

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