Discipline: 
Mathematics
Status: 
Available
Level: 
PhD Project
Level: 
Masters Project
Level: 
Honours Project
Supervisor(s): 
Professor Geoff Mclachlan

Traditional methods of analysing flow cytometry data rely on subjective manual gating. As modern day machines can now provide data on up to 20 markers simultaneously, an automated approach is required.  In this project the aim is to investigate the relative performance of various methods that have been proposed in recent times for the automatic analysis of flow cytometry data.