Automated analysis of high dimensional flow cytometry data

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Background The recent increase in the amount of data generated by flow cytometry (FCM) poses unique informatics and statistical challenges. It is widely recognized that one basic challenge for these technologies is to simplify the extraction of data and statistical information. Traditionally, the majority of FCM experiments have been analyzed visually, through time consuming and subjective serial inspection of one or two dimensions at a time. Progress in research activities from the previous grant (2013+) In contrast to manual analysis, our progress over the last six years has shown computational analysis can be robust, reproducible and rapid. We completed the short term objectives of the previous of the NSERC DG through the development of algorithms for: (1) Supervised cell population identification; (2) The top-performing machine learning approach for unsupervised cell population identification; (3) The top-performing biomarker discovery algorithm; (4) A unsupervised method for cell population identification can incorporate data from multiple tube samples to identify cell populations. We undertook extensive KT activities to apply these algorithms to complex datasets from a worldwide network of collaborators to test their hypothesis on such matters as the mechanism of action of compounds. Our results support the Objectives of the current proposal. Objectives Long term objective: Develop a user-friendly, robust, free/open source computational platform for the high-throughput analysis of FCM data that becomes widely applied for Natural Science research. Short-term objectives: Objective 1. Develop a robust approach to match cell populations profiles in common across samples to uncover previously unknown relationships between groups of unlabelled samples. Objective 2. Extend flowType to support HPC by implementing parallelization Objective 3. Improve the performance of flowType through new data summary statistics. Objective 4. Apply algorithms to collaborator's natural science datasets. Methodology The short-term objectives will be completed through the efforts of 2 PhD students, supported by undergraduates. Impact The impact of my academic career will be significantly larger as a result of community efforts leveraging the algorithms we propose to develop than any I could accomplish solely on my own. This is true also within the context of this proposal. Our development of an approach to match cell population profiles will be immediately applied in the context of the International Mouse Phenotyping Consortium, a $900M effort targeted to the identification of the function of every gene in a mammalian genome. Our work with Genentech in the development of the next iteration of flowType/RchyOptimyx will immediately be applied to understand the mechanisms underlying how new chemical compounds affect the immune system. All HQP develop skills as data scientists, addressing the high demand for scientists in this area in industry and academia.