Qian, YuKim, HyunsooPurawat, ShwetaWang, JianwuStanton, RickLee, AlexandraXu, WeijiaAltintas, IlkaySinkovits, RobertScheuermann, Richard H.2024-02-142024-02-142015-07-26Qian, Yu, Hyunsoo Kim, Shweta Purawat, Jianwu Wang, Rick Stanton, Alexandra Lee, Weijia Xu, Ilkay Altintas, Robert Sinkovits, and Richard H. Scheuermann. “FlowGate: Towards Extensible and Scalable Web-Based Flow Cytometry Data Analysis.” In Proceedings of the 2015 XSEDE Conference: Scientific Advancements Enabled by Enhanced Cyberinfrastructure, 1–8. XSEDE ’15. New York, NY, USA: Association for Computing Machinery, 2015. https://doi.org/10.1145/2792745.2792750.https://doi.org/10.1145/2792745.2792750http://hdl.handle.net/11603/31622XSEDE '15: Extreme Science Engineering Discovery Environment 2015 Conference St. Louis Missouri July 26 - 30, 2015Recent advances in cytometry instrumentation are enabling the generation of "big data" at the single cell level for the identification of cell-based biomarkers, which will fundamentally change the current paradigm of diagnosis and personalized treatment of immune system disorders, cancers, and blood diseases. However, traditional flow cytometry (FCM) data analysis based on manual gating cannot effectively scale to address this new level of data generation. Computational data analysis methods have recently been developed to cope with the increasing data volume and dimensionality generated from FCM experiments. Making these computational methods easily accessible to clinicians and experimentalists is one of the biggest challenges that algorithm developers and bioinformaticians need to address. This paper describes FlowGate, a novel prototype cyberinfrastructure for web-based FCM data analysis, which integrates graphical user interfaces (GUI), workflow engines, and parallel computing resources for extensible and scalable FCM data analysis. The goal of FlowGate is to allow users to easily access state-of-the-art FCM computational methods developed using different programming languages and software on the same platform, when the implementations of these methods follow standardized I/O. By adopting existing data and information standards, FlowGate can also be integrated as the back-end data analytical platform with existing immunology and FCM databases. Experimental runs of two representative FCM data analytical methods in FlowGate on different cluster computers demonstrated that the task runtime can be reduced linearly with the number of compute cores used in the analysis.8 pagesen-USThis item is likely protected under Title 17 of the U.S. Copyright Law. Unless on a Creative Commons license, for uses protected by Copyright Law, contact the copyright holder or the author.Flow cytometrySingle cell data analysisCyberinfrastructureworkflow engineparallel computingUMBC Big Data Analytics LabText