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Hierarchical Modeling for Rare Event Detection and Cell Subset Alignment across Flow Cytometry Samples

Figure 1

Schematic summary illustrating the HDP model framework.

A graphical model provides a declarative representation of the HDPGMM. The figure shows a compact plate representation of the graphical model, in which plates (rounded rectangles) are used to group variables in a subgraph. Each subgraph in a plate is replicated a number of times as indicated by the label within the plate. The event in the sample is represented by , and the component for the sample is a multivariate Gaussian with proportion , mean and covariance matrix .s. Hyper-parameters that can be set are , , , , and as described in Methods. Given the declarative graphical model, standard and GPU-accelerated MCMC sampling algorithms can be used to implement the model as previously described [16].

Figure 1

doi: https://doi.org/10.1371/journal.pcbi.1003130.g001