Detecting T cell activation using a varying dimension Bayesian model. Issue 4 (12th March 2018)

• Record Type: Journal Article
• Authors: Hu, Zicheng; Lancaster, Jessica N; Ehrlich, Lauren I. R.; Müller, Peter
• Is Part Of: Journal of applied statistics. Volume 45:Issue 4(2018)
• Page Start: 697
• Page End: 713
• Publication Date: 2018-03-12
• Subjects: Bayesian -- Indo-1 -- MCMC -- pseudo-prior -- T cell activation -- two photon microscopy; Statistics -- Periodicals; 519.5
• DOI: 10.1080/02664763.2017.1290789
• Languages: English
• ISSNs: 0266-4763

Abstract

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.