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Advances in Bioinformatics
Volume 2009 (2009), Article ID 247646, 12 pages
http://dx.doi.org/10.1155/2009/247646
Research Article

Merging Mixture Components for Cell Population Identification in Flow Cytometry

1Computational Biology Unit, Clinical Research Institute of Montreal, 110 Pine Avenue West, Montreal, QC, H2W1R7, Canada
2Terry Fox Laboratory, BC Cancer Research Center, Vancouver, BC, Canada V5Z 1L3
3Département de Biochimie, Université de Montreal, Montreal, QC, Canada

Received 7 April 2009; Revised 6 July 2009; Accepted 22 August 2009

Academic Editor: George Luta

Copyright © 2009 Greg Finak 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.

Abstract

We present a framework for the identification of cell subpopulations in flow cytometry data based on merging mixture components using the flowClust methodology. We show that the cluster merging algorithm under our framework improves model fit and provides a better estimate of the number of distinct cell subpopulations than either Gaussian mixture models or flowClust, especially for complicated flow cytometry data distributions. Our framework allows the automated selection of the number of distinct cell subpopulations and we are able to identify cases where the algorithm fails, thus making it suitable for application in a high throughput FCM analysis pipeline. Furthermore, we demonstrate a method for summarizing complex merged cell subpopulations in a simple manner that integrates with the existing flowClust framework and enables downstream data analysis. We demonstrate the performance of our framework on simulated and real FCM data. The software is available in the flowMerge package through the Bioconductor project.