Abstract

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.

  Funding Agency

Agency
National Institute of Health (NIH)
Institute
National Institute of Biomedical Imaging and Bioengineering (NIBIB)
Type
Research Project (R01)
Project #
5R01EB008400-02
Application #
7577491
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
2009-05-01
Budget End
2010-04-30
Support Year
2
Fiscal Year
2009
Total Cost
$342,223
Indirect Cost

  Institution

Name
Fred Hutchinson Cancer Research Center
Department
Type
DUNS #
078200995
City
Seattle
State
WA
Country
United States
Zip Code
98109

  Publications

Johnstone, Jennie; Parsons, Robin; Botelho, Fernando et al. (2017) T-Cell Phenotypes Predictive of Frailty and Mortality in Elderly Nursing Home Residents. J Am Geriatr Soc 65:153-159
Pedersen, Natasja Wulff; Chandran, P Anoop; Qian, Yu et al. (2017) Automated Analysis of Flow Cytometry Data to Reduce Inter-Lab Variation in the Detection of Major Histocompatibility Complex Multimer-Binding T Cells. Front Immunol 8:858
Slichter, Chloe K; McDavid, Andrew; Miller, Hannah W et al. (2016) Distinct activation thresholds of human conventional and innate-like memory T cells. JCI Insight 1:
Finak, Greg; Gottardo, Raphael (2016) Promises and Pitfalls of High-Throughput Biological Assays. Methods Mol Biol 1415:225-43
Hsiao, Chiaowen; Liu, Mengya; Stanton, Rick et al. (2016) Mapping cell populations in flow cytometry data for cross-sample comparison using the Friedman-Rafsky test statistic as a distance measure. Cytometry A 89:71-88
Fletez-Brant, Kipper; Špidlen, Josef; Brinkman, Ryan R et al. (2016) flowClean: Automated identification and removal of fluorescence anomalies in flow cytometry data. Cytometry A 89:461-71
McDavid, Andrew; Finak, Greg; Gottardo, Raphael (2016) The contribution of cell cycle to heterogeneity in single-cell RNA-seq data. Nat Biotechnol 34:591-3
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
Courtot, Mélanie; Meskas, Justin; Diehl, Alexander D et al. (2015) flowCL: ontology-based cell population labelling in flow cytometry. Bioinformatics 31:1337-9

Showing the most recent 10 out of 48 publications