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Advances in BioinformaticsVolume 2009 2009, Article ID 584603, 19 pages

Review ArticleTerry Fox Laboratory, British Columbia Cancer Agency, Vancouver, BC, Canada V5Z 1L3

Received 1 May 2009; Revised 20 July 2009; Accepted 22 August 2009

Academic Editor: George Luta

Copyright © 2009 Ali Bashashati and Ryan R. Brinkman. 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.


Flow cytometry FCM is widely used in health research and in treatment for a variety of tasks, such as in the diagnosis and monitoring of leukemia and lymphoma patients, providing the counts of helper-T lymphocytes neededto monitor the course and treatment of HIV infection, the evaluation of peripheral blood hematopoietic stem cellgrafts, and many other diseases. In practice, FCM data analysis is performed manually, a process that requires aninordinate amount of time and is error-prone, nonreproducible, nonstandardized, and not open for re-evaluation,making it the most limiting aspect of this technology. This paper reviews state-of-the-art FCM data analysisapproaches using a framework introduced to report each of the components in a data analysis pipeline. Currentchallenges and possible future directions in developing fully automated FCM data analysis tools are also outlined.

Autor: Ali Bashashati and Ryan R. Brinkman

Fuente: https://www.hindawi.com/


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