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Scurll, Joshua M.

University of British Columbia

Detecting clusters of data points in physical or high-dimensional (HD) space is a common task in biology and biomedicine. Single-molecule localization microscopy (SMLM), a category of super-resolution microscopy, is often used to analyze spatial distributions of proteins on the surface membranes of, or inside, biological cells. Proteins sometimes need to form clusters to surpass a critical signalling threshold for functional activity. Therefore, investigating protein clustering can yield important insights about protein and cell functions in health and disease. Mass cytometry, also called CyTOF, is a high-throughput technique for investigating the abundance of multiple proteins simultaneously in single cells, resulting in HD data in which cells cluster into different phenotypes. Cluster analysis of CyTOF data is important for understanding heterogeneity in biological cell populations, which has clinical implications in cancer biology. This dissertation first describes a new method, called StormGraph, to detect clusters in diverse SMLM data. StormGraph converts 2D or 3D SMLM data to a weighted graph, applies a community detection algorithm to assign localizations to clusters at multiple scales, and includes a new algorithm to generate a single-level clustering from a multi-level cluster hierarchy. Unlike most other clustering algorithms, StormGraph utilizes uncertainties associated with point positions. Results of using SMLM and StormGraph to analyze clustering of B-cell antigen receptors on the membranes of normal and malignant B cells are presented. Next, this dissertation describes a new measure of similarity between clusters in HD data. Computed by a method called ASTRICS, it is based on local dimensionality reduction and triangulation of alpha shapes. A strategy for clustering and visualizing HD data, with ASTRICS used to construct a graph from an initial set of fine-grained clusters, is presented and demonstrated on three very different HD datasets, including public CyTOF data. Finally, new CyTOF experiments were designed and performed to analyze heterogeneity among diffuse large B-cell lymphoma (DLBCL) cell lines. Results of the analysis, including clustering and visualization using the strategy based on ASTRICS, are presented. Most interesting were revelations about signalling dynamics linked to the cell cycle, which differed between DLBCL subtypes.

Text

2021-07-29

Attribution-NonCommercial-ShareAlike 4.0 International

http://hdl.handle.net/2429/79142

Mathematics

University of British Columbia

UBCV

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