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Automated analysis of big flow cytometry data

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Generating solutions

Status

Active

Competition

2015 Bioinformatics and Computational Biology B/CB

Genome Centre(s)

GE3LS

No

Project Leader(s)

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.

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