Advanced real-time classification methods for flow cytometry data analysis and cell sorting

2002 ◽  
Author(s):  
James F. Leary ◽  
Lisa M. Reece ◽  
James A. Hokanson ◽  
Judah I. Rosenblatt
Keyword(s):  
Flow Cytometry ◽  
Data Analysis ◽  
Real Time ◽  
Cell Sorting ◽  
Classification Methods ◽  
Flow Cytometry Data ◽  
Real Time Classification

scholarly journals Application of neural networks to flow cytometry data analysis and real-time cell classification

Cytometry ◽  
1996 ◽  
Vol 23 (4) ◽  
pp. 290-302 ◽  
Author(s):  
Donald S. Frankel ◽  
Sheila L. Frankel ◽  
Brian J. Binder ◽  
Robert F. Vogt
Keyword(s):  
Neural Networks ◽  
Flow Cytometry ◽  
Data Analysis ◽  
Real Time ◽  
Cell Classification ◽  
Flow Cytometry Data

scholarly journals OpenCyto: An Open Source Infrastructure for Scalable, Robust, Reproducible, and Automated, End-to-End Flow Cytometry Data Analysis

2014 ◽  
Vol 10 (8) ◽  
pp. e1003806 ◽  
Author(s):  
Greg Finak ◽  
Jacob Frelinger ◽  
Wenxin Jiang ◽  
Evan W. Newell ◽  
John Ramey ◽  
...  
Keyword(s):  
Flow Cytometry ◽  
Data Analysis ◽  
Open Source ◽  
Flow Cytometry Data ◽  
End To End

scholarly journals An open-source solution for advanced imaging flow cytometry data analysis using machine learning

Methods ◽  
2017 ◽  
Vol 112 ◽  
pp. 201-210 ◽  
Author(s):  
Holger Hennig ◽  
Paul Rees ◽  
Thomas Blasi ◽  
Lee Kamentsky ◽  
Jane Hung ◽  
...  
Keyword(s):  
Machine Learning ◽  
Flow Cytometry ◽  
Data Analysis ◽  
Open Source ◽  
Advanced Imaging ◽  
Flow Cytometry Data ◽  
Imaging Flow Cytometry

scholarly journals A Survey of Flow Cytometry Data Analysis Methods

2009 ◽  
Vol 2009 ◽  
pp. 1-19 ◽  
Author(s):  
Ali Bashashati ◽  
Ryan R. Brinkman
Keyword(s):  
Flow Cytometry ◽  
Data Analysis ◽  
State Of The Art ◽  
Analysis Pipeline ◽  
Future Directions ◽  
Hematopoietic Stem ◽  
Flow Cytometry Data ◽  
Helper T Lymphocytes ◽  
Data Analysis Pipeline ◽  
Data Analysis Methods

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.

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