Article

Mixture Models for Single Cell Assays with Applications to VaccineStudies

Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center (FHCRC), Seattle, WA 98109, USA.
Biostatistics (Impact Factor: 2.24). 07/2013; DOI: 10.1093/biostatistics/kxt024
Source: arXiv

ABSTRACT In immunological studies, the characterization of small, functionally
distinct cell subsets from blood and tissue is crucial to decipher system level
biological changes. An increasing number of studies rely on assays that provide
single-cell measurements of multiple genes and proteins from bulk cell samples.
A common problem in the analysis of such data is to identify biomarkers (or
combinations of thereof) that are differentially expressed between two
biological conditions (e.g., before/after vaccination), where expression is
defined as the proportion of cells expressing the biomarker or combination in
the cell subset of interest.
Here, we present a Bayesian hierarchical framework based on a beta-binomial
mixture model for testing for differential biomarker expression using
single-cell assays. Our model allows inference to be subject specific, as is
typically required when accessing vaccine responses, while borrowing strength
across subjects through common prior distributions. We propose two approaches
for parameter estimation: an empirical-Bayes approach using an
Expectation-Maximization algorithm and a fully Bayesian one based on a Markov
chain Monte Carlo algorithm. We compare our method against frequentist
approaches for single-cell assays including Fisher's exact test, a likelihood
ratio test, and basic log-fold changes. Using several experimental assays
measuring proteins or genes at the single-cell level and simulated data, we
show that our method has higher sensitivity and specificity than alternative
methods. Additional simulations show that our framework is also robust to model
misspecification. Finally, we also demonstrate how our approach can be extended
to testing multivariate differential expression across multiple biomarker
combinations using a Dirichlet-multinomial model and illustrate this
multivariate approach using single-cell gene expression data and simulations.

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