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Computational techniques for flow cytometry : the application for automated analysis of innate immune response flow cytometry data.

University of British Columbia

Flow cytometry (FCM) is a technique for measuring physical, chemical and biological characteristics of individual cells. Recent advances in FCM have provided researchers with the facility to improve their understanding of the tremendously complex immune system. However, the technology is hampered by current manual analysis methodologies. In this thesis, I developed computational methods for the automated analysis of immune response FCM data to address this bottleneck. I hypothesized that highly accurate results could be obtained through learning from the patterns that a biology expert applies when doing the analysis manually. In FCM data analysis, it is often desirable to identify homogeneous subsets of cells within a sample. Traditionally, this is done through manual gating, a procedure that can be subjective and time-consuming. I developed SamSPECTRAL, an automated spectral-based clustering algorithm to identify FCM cell populations of any shape, size and distribution while addressing the drawbacks of manual gating. A particularly signi cant achievement of SamSPECTRAL was its successful performance in nding rare cell populations. Similarly, in most FCM applications, it is required to match similar cell populations between di erent FCM samples. I developed a novel learning-based cluster matching method that incorporates domain expert knowledge to nd the best matches of target populations among all clusters generated by a clustering algorithm. Immunophenotyping of immune cells and measuring cytokine responses are two main components of immune response FCM data analysis. I combined the SamSPECTRAL algorithm and cluster matching to perform automated immunophenotyping. I also devised a method to measure cytokine responses automatically. After developing computational methods for each of the above analysis components separately, I organized them into a semi-automated pipeline, so they all work together as a uni ed package. My experiments on 216 FCM samples con rmed that my semi-automated pipeline can reproduce manual analysis results highly accurately both for immunophenotyping and measuring cytokine responses. My other main contributions were correlation analysis of intracellular and secreted cytokines, and developing a formula called GiMFI to improve measuring functional response of cytokine-producing cells using ow cytometry assay.

Text

2013-04-30

Attribution-NonCommercial-NoDerivatives 4.0 International

http://hdl.handle.net/2429/42179

Computer Science

University of British Columbia

UBCV

http://creativecommons.org/licenses/by-nc-nd/4.0/