Detecting T cell activation using a varying dimension Bayesian model

Zicheng Hu, Jessica N Lancaster, Lauren I. R. Ehrlich and Peter Müller

Journal of Applied Statistics, 2018, vol. 45, issue 4, 697-713

Abstract: The detection of T cell activation is critical in many immunological assays. However, detecting T cell activation in live tissues remains a challenge due to highly noisy data. We developed a Bayesian probabilistic model to identify T cell activation based on calcium flux, a increase in intracellular calcium concentration that occurs during T cell activation. Because a T cell has unknown number of flux events, the implementation of posterior inference requires trans-dimensional posterior simulation. The model is able to detect calcium flux events at the single cell level from simulated data, as well as from noisy biological data.

Date: 2018
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DOI: 10.1080/02664763.2017.1290789

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