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Flow cytometry (FCM) is the standard multi-parameter assay for measuringsingle cell phenotype and functionality. It is commonly used for quantifyingthe relative frequencies of cell subsets in blood and disaggregated tissues. Atypical analysis of FCM data involves cell classification---that is, theidentification of cell subgroups in the sample---and comparisons of the cellsubgroups across samples or conditions. While modern experiments oftennecessitate the collection and processing of samples in multiple batches,analysis of FCM data across batches is challenging because differences acrosssamples may occur due to either true biological variation or technical reasonssuch as antibody lot effects or instrument optics across batches. Thus acritical step in comparative analyses of multi-sample FCM data---yet missing inexisting automated methods for analyzing such data---is cross-samplecalibration, whose goal is to align corresponding cell subsets across multiplesamples in the presence of technical variations, so that biological variationscan be meaningfully compared. We introduce a Bayesian nonparametrichierarchical modeling approach for accomplishing both calibration and cellclassification simultaneously in a unified probabilistic manner. Threeimportant features of our method make it particularly effective for analyzingmulti-sample FCM data: a nonparametric mixture avoids prespecifying the numberof cell clusters; a hierarchical skew normal kernel that allows flexibility inthe shapes of the cell subsets and cross-sample variation in their locations;and finally the "coarsening" strategy makes inference robust to departures fromthe model such as heavy-tailness not captured by the skew normal kernels. Wedemonstrate the merits of our approach in simulated examples and carry out acase study in the analysis of two multi-sample FCM data sets.

Coarsened mixtures of hierarchical skew normal kernels for flow cytometry analyses