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Abstract:
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Intracellular Cytokine Staining (ICS) - a type of cytometry experiment used to measure cytokine production at the single cell level - is an important measure used in immune monitoring and vaccine development. A well known challenge in analyzing flow cytometry data is that they are prone to batch and technical variation, but also produce many correlated features (cell subsets). These effects are often ignored; cell subsets are treated independently, counts are modeled as proportions, and batch effects are not systematically accounted for. We propose a generalized estimating equation modeling framework for analyzing cytometry count data, allowing for the screening of cell populations while accounting for both technical and biological nuisance factors. We account for the overdispersion often observed when modeling small counts by using the beta-binomial distribution. To account for the within subject dependence we estimate an unstructured working correlation and robust standard errors, which are then shrunk towards model based estimates to increase stability. We demonstrate our methodology by applying it to experimental assays measuring cytokine expression at the single-cell level.
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