Detecting T cell activation using a varying dimension Bayesian model

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

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

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.

Original languageEnglish (US)
Pages (from-to)697-713
Number of pages17
JournalJournal of Applied Statistics
Volume45
Issue number4
DOIs
StatePublished - Mar 12 2018

Keywords

  • Bayesian
  • Indo-1
  • MCMC
  • T cell activation
  • pseudo-prior
  • two photon microscopy

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

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