Clustering of cell populations in flow cytometry data using a combination of Gaussian mixtures

We propose a supervised learning approach to automatic quantification of cell populations in flow cytometric samples. One sample contains up to millions of measurement vectors with a dimensionality between 10 and 20. Normally, each measurement vector corresponds to a single cell in the biological sa...

Full description

Bibliographic Details

Published in:	Pattern recognition Vol. 60; pp. 1029 - 1040
Main Authors:	Reiter, Michael, Rota, Paolo, Kleber, Florian, Diem, Markus, Groeneveld-Krentz, Stefanie, Dworzak, Michael
Format:	Journal Article
Language:	English
Published:	Elsevier Ltd 01.12.2016
Subjects:	Acute lymphblastic leukemia Clustering Convex non-negative matrix factorization Flow cytometry Gaussian mixture model L2 distance Flow cytometry L2 distance Convex non-negative matrix factorization Clustering Acute lymphblastic leukemia Gaussian mixture model
ISSN:	0031-3203, 1873-5142
Online Access:	Get full text
Tags:	Add Tag No Tags, Be the first to tag this record!