Mathematics Colloquia and Seminars

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Complex protein interaction networks complicate the understanding of what most promotes the rate of cancer progression. High dimensional data provides opportunities for new insights into possible mechanisms for the proliferative nature of aggressive cancers, but these datasets often require fresh techniques and ideas for exploration and analysis. In this talk, we consider mass cytometry data capturing expression levels of tens of biomarkers in individual cells from acute myeloid leukemia patients. After identifying immune cell subpopulations in this data using an established clustering method, we present a novel statistic for testing differential biomarker correlations across patients and within specific cell phenotypes.