Statistical Methods for High Through-Put Flow Cytometry

This research project aims to develop automated statistical methodology to analyze data generated by flow cytometry (FCM), a high-throughput technology widely used in health research and disease diagnostics. FCM is commonly used to define the overall status of the immune system either in healthy or diseased subjects by performing quantitative measurements on a variety of cell types belonging to the immune system. However, FCM has not reached its full potential due to the lack of tools to process the wealth of data generated by this technique as part of an automated analysis platform that parallels the data generation platform. In this proposal, the intern plans to develop new statistical techniques to address the current bottleneck in the application of the recently developed high throughput flow cytometry technology which is poised to have a dramatic impact on human health.

Intern: 
Kenneth Lo
Faculty Supervisor: 
Dr. Raphael Gottardo
Province: 
British Columbia
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