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.
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.
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