Flow cytometry is a single-cell measurement technology that is data-rich and plays a critical role in basic research and clinical diagnostics. The volume and dimensionality of data sets currently produced with modern instrumentation is orders of magnitude greater than in the past. Automated analysis methods in the field have made great progress in the past five years. The tools are available to perform automated cell population identification, but the infrastructure, methods and data standards do not yet exist to integrate and compare non-standardized big flow cytometry data sets available in public repositories. This proposal will develop the data standards, software infrastructure and computational methods to enable researchers to leverage the large amount of public cytometry data in order to integrate, re-analyze, and draw novel biological insights from these data sets. The impact of this project will be to provide researchers with tools that can be used to bridge the gap between inference from isolated single experiments or studies, to insights drawn from large data sets from cross-study analysis and multi-center trials.

Public Health Relevance

The aims of this project are to develop standards, software and methods for integrating and analyzing big and diverse flow cytometry data sets. The project will enable users of cytometry to directly compare diverse and non-standardized cytometry data to each other and make biological inferences about them. The domain of application spans all disease areas where cytometry is utilized.

Agency
National Institute of Health (NIH)
Institute
National Institute of General Medical Sciences (NIGMS)
Type
Research Project (R01)
Project #
5R01GM118417-03
Application #
9731544
Study Section
Biodata Management and Analysis Study Section (BDMA)
Program Officer
Ravichandran, Veerasamy
Project Start
2017-09-20
Project End
2022-06-30
Budget Start
2019-07-01
Budget End
2020-06-30
Support Year
3
Fiscal Year
2019
Total Cost
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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Lux, Markus; Brinkman, Ryan Remy; Chauve, Cedric et al. (2018) flowLearn: fast and precise identification and quality checking of cell populations in flow cytometry. Bioinformatics 34:2245-2253
Finak, Greg (2018) The Computational article format: Software as a research output. Cytometry A 93:1187-1188