Flow cytometry is a data-rich technology that plays a critical role in basic research and clinical therapy for a variety of human diseases. Recent technological developments have greatly increased the areas of application and data throughput, and corresponding innovative analysis methods are needed. In order to be able to take advantage of these new capabilities researchers need access to high quality analysis tools that will help to identify subpopulations of cells with particular characteristics. The methods we are proposing include advanced methods for machine learning and visualization. We will apply our methods to a number of different scenarios such as the analysis of longitudinal data, and the analysis of data arising from clinical studies.

Public Health Relevance

The aims of this project are to provide statistical and computational methods for the analysis of flow cytometry data. The impact of these tools will be to provide better, more reliable, tools for the analysis of flow cytometry data. The domain of application spans all diseases, but current applications are focused on HIV disease and cancer. ? ? ?

Agency
National Institute of Health (NIH)
Institute
National Institute of Biomedical Imaging and Bioengineering (NIBIB)
Type
Research Project (R01)
Project #
1R01EB008400-01
Application #
7431959
Study Section
Special Emphasis Panel (ZRG1-BST-Q (91))
Program Officer
Korte, Brenda
Project Start
2008-05-01
Project End
2012-04-30
Budget Start
2008-05-01
Budget End
2009-04-30
Support Year
1
Fiscal Year
2008
Total Cost
$376,423
Indirect Cost
Name
Fred Hutchinson Cancer Research Center
Department
Type
DUNS #
078200995
City
Seattle
State
WA
Country
United States
Zip Code
98109
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Finak, Greg; Langweiler, Marc; Jaimes, Maria et al. (2016) Standardizing Flow Cytometry Immunophenotyping Analysis from the Human ImmunoPhenotyping Consortium. Sci Rep 6:20686
Aghaeepour, Nima; Chattopadhyay, Pratip; Chikina, Maria et al. (2016) A benchmark for evaluation of algorithms for identification of cellular correlates of clinical outcomes. Cytometry A 89:16-21
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Malek, Mehrnoush; Taghiyar, Mohammad Jafar; Chong, Lauren et al. (2015) flowDensity: reproducing manual gating of flow cytometry data by automated density-based cell population identification. Bioinformatics 31:606-7

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