Merging Mixture Components for Cell Population Identification in Flow Cytometry
Joint Authors
Finak, Greg
Bashashati, Ali
Brinkman, Ryan R.
Source
Issue
Vol. 2009, Issue 2009 (31 Dec. 2009), pp.1-12, 12 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2009-11-12
Country of Publication
Egypt
No. of Pages
12
Main Subjects
Natural & Life Sciences (Multidisciplinary)
Biology
Abstract EN
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.
American Psychological Association (APA)
Finak, Greg& Bashashati, Ali& Brinkman, Ryan R.. 2009. Merging Mixture Components for Cell Population Identification in Flow Cytometry. Advances in Bioinformatics،Vol. 2009, no. 2009, pp.1-12.
https://search.emarefa.net/detail/BIM-457125
Modern Language Association (MLA)
Finak, Greg…[et al.]. Merging Mixture Components for Cell Population Identification in Flow Cytometry. Advances in Bioinformatics No. 2009 (2009), pp.1-12.
https://search.emarefa.net/detail/BIM-457125
American Medical Association (AMA)
Finak, Greg& Bashashati, Ali& Brinkman, Ryan R.. Merging Mixture Components for Cell Population Identification in Flow Cytometry. Advances in Bioinformatics. 2009. Vol. 2009, no. 2009, pp.1-12.
https://search.emarefa.net/detail/BIM-457125
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-457125