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Advances in Bioinformatics
Volume 2009, Article ID 686759, 7 pages
Research Article

Automatic Clustering of Flow Cytometry Data with Density-Based Merging

1Department of Statistics, Stanford University, Stanford, CA 94305, USA
2Department of Genetics, Stanford University, Stanford, CA 94305, USA

Received 1 May 2009; Revised 27 July 2009; Accepted 25 August 2009

Academic Editor: Raphael Gottardo

Copyright © 2009 Guenther Walther et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


The ability of flow cytometry to allow fast single cell interrogation of a large number of cells has made this technology ubiquitous and indispensable in the clinical and laboratory setting. A current limit to the potential of this technology is the lack of automated tools for analyzing the resulting data. We describe methodology and software to automatically identify cell populations in flow cytometry data. Our approach advances the paradigm of manually gating sequential two-dimensional projections of the data to a procedure that automatically produces gates based on statistical theory. Our approach is nonparametric and can reproduce nonconvex subpopulations that are known to occur in flow cytometry samples, but which cannot be produced with current parametric model-based approaches. We illustrate the methodology with a sample of mouse spleen and peritoneal cavity cells.