Merging Mixture Components for Cell Population Identification in Flow Cytometry
Open Access
- 12 November 2009
- journal article
- research article
- Published by Hindawi Limited in Advances in Bioinformatics
- Vol. 2009, 1-12
- https://doi.org/10.1155/2009/247646
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.Keywords
Funding Information
- Natural Sciences and Engineering Research Council of Canada (EB008400)
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