A non-negative multilinear block tensor decomposition approach to flow cytometry data analysis

Author(s):  
David Brie ◽  
Sebastian Miron ◽  
Philippe Becuwe ◽  
Stephanie Grandemange
2014 ◽  
Vol 10 (8) ◽  
pp. e1003806 ◽  
Author(s):  
Greg Finak ◽  
Jacob Frelinger ◽  
Wenxin Jiang ◽  
Evan W. Newell ◽  
John Ramey ◽  
...  

Methods ◽  
2017 ◽  
Vol 112 ◽  
pp. 201-210 ◽  
Author(s):  
Holger Hennig ◽  
Paul Rees ◽  
Thomas Blasi ◽  
Lee Kamentsky ◽  
Jane Hung ◽  
...  

2009 ◽  
Vol 2009 ◽  
pp. 1-19 ◽  
Author(s):  
Ali Bashashati ◽  
Ryan R. Brinkman

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 needed to monitor the course and treatment of HIV infection, the evaluation of peripheral blood hematopoietic stem cell grafts, and many other diseases. In practice, FCM data analysis is performed manually, a process that requires an inordinate 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 analysis approaches using a framework introduced to report each of the components in a data analysis pipeline. Current challenges and possible future directions in developing fully automated FCM data analysis tools are also outlined.


2013 ◽  
Vol 10 (3) ◽  
pp. 228-238 ◽  
Author(s):  
Nima Aghaeepour ◽  
◽  
Greg Finak ◽  
Holger Hoos ◽  
Tim R Mosmann ◽  
...  

2014 ◽  
Vol 85 (11) ◽  
pp. 969-977 ◽  
Author(s):  
Juliet Frederiksen ◽  
Marcus Buggert ◽  
Annika C. Karlsson ◽  
Ole Lund

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