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Automatic analysis of flow cytometry data and its application to lymphoma diagnosis

University of British Columbia

Flow cytometry has many applications in clinical medicine and biological research. For many modern applications, traditional methods of manual data interpretation are not efficient due to the large amount of complex, high dimensional data. In this thesis, I discuss some of the important challenges towards automatic analysis of flow cytometry data and propose my solutions. To validate my approach on addressing real life problems, I developed an automatic pipeline for analyzing flow cytometry data and applied it to clinical data. My pipeline can potentially be useful for improving quality check on diagnosis, assisting discovery of novel phenotypes, and making clinical recommendations. Furthermore, some of the challenges that I studied are rooted in more general areas of computer science, and therefore, the tools and techniques that I developed can be applied to a wider range of problems in data mining and machine learning. Enhancement to spectral clustering algorithm and proposing a novel scheme for scoring features are two examples of my contributions to computer science that were developed as part of this thesis.

Text

2011-12-13

Attribution-NonCommercial 3.0 Unported

http://hdl.handle.net/2429/39660

Computer Science

University of British Columbia

UBCV

http://creativecommons.org/licenses/by-nc/3.0/