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Automated analysis of big flow cytometry data

Status: 
Active
Competition: 
2015 Bioinformatics and Computational Biology B/CB
Sector: 
Health
Genome Centre(s):
Genome British Columbia
Project Leader(s):
Ryan Brinkman (British Columbia Cancer Agency), Cedric Chauve (Simon Fraser University), Sara Mostafavi (University of British Columbia)
Fiscal Year Project Launched: 
2016-2017
Project Description: 

Flow cytometry (FCM) works by suspending cells in a fluid and then passing that fluid through a laser. FCM-based assays are now widely applied in health research and are important for diagnosing blood cancers, monitoring viral infection (e.g., HIV), vaccine development and stem cell research. As FCM technology has improved over the years the amount of information it delivers per cell has increased five-fold, to the point it is virtually impossible to analyze manually without missing much of the information embedded in those cells. In order to unleash the power of big FCM data, analytical tools and statistical methods must be developed and made widely available.

Drs. Ryan Brinkman (BC Cancer Agency), Cedric Chauve (Simon Fraser University) and Sara Mostafavi (UBC) are developing an easy-to-use, graph-based approach to represent the information generated by FCM as well as new approaches for supervised and unsupervised cell population identification . Among other benefits, users will be able to identify novel patient groups, such as those who don’t respond to certain drugs, enabling the delivery of truly personalized medicine.