Dr. Daniel Merl (Duke University Department of Statistical Science)

Title: Nonparametric mixtures of nonparametric mixtures for detecting cell subtypes in flow cytometry

Abstract: Flow cytometry is a high throughput experimental methodology for measuring the expression of surface proteins on hundreds of thousands to millions of individual cells.  Identification of distinct cellular subtypes on the basis of these multivariate expression patterns plays an important role in adjuvant vaccine design, for which the goal is to elicit the strongest possible immune response.  Due to the sparse and highly non-Gaussian nature of flow cytometric data, identification and quantification of cellular subtypes has traditionally (and perhaps
astonishingly) been accomplished through manual gating based on 2-d projections.  Bayesian nonparametrics provides a flexible, model-based, predictive framework for multivariate non-Gaussian density estimation and classification.   However, most existing nonparametric methods assume the fundamental mixture components to be of some standard distributional form that are individually insufficient to describe variation in the cell subtypes.  I present a novel hierarchical mixture model, a nonparametric mixture of nonparametric mixtures, that enables automatic registration of an unknown number non-Gaussian components, each of which is itself a mixture of an unknown number of basis distributions.  I will discuss inferential methods capable of exploiting high performance computing clusters, and apply the methodology to assess treatment efficacy in an adjuvant vaccine trial data set.