Background: High-dimensional flow cytometry and mass cytometry allow systemic-level characterization of more than 10 protein profiles at single-cell resolution and provide a much broader landscape in many biological applications, such as disease diagnosis and prediction of clinical outcome. When associating clinical information with cytometry data, traditional approaches require two distinct steps for identification of cell populations and statistical test to determine whether the difference between two population proportions is significant. These two-step approaches can lead to information loss and analysis bias.
Results: We propose a novel statistical framework, called LAMBDA (Latent Allocation Model with Bayesian Data Analysis), for simultaneous identification of unknown cell populations and discovery of associations between these populations and clinical information. LAMBDA uses specified probabilistic models designed for modeling the different distribution information for flow or mass cytometry data, respectively. We use a zero-inflated distribution for the mass cytometry data based the characteristics of the data. A simulation study confirms the usefulness of this model by evaluating the accuracy of the estimated parameters. We also demonstrate that LAMBDA can identify associations between cell populations and their clinical outcomes by analyzing real data. LAMBDA is implemented in R and is available from GitHub ( https://github.com/abikoushi/lambda ).
Keywords: Bayesian mixture model; Flow cytomety; Mass cytometory; Stochastic EM algorithm.