Analysis of Proteomics Data: Bayesian Alignment of Functions

• Activity Number: 421
• Type: Contributed
• Date/Time: Tuesday, August 11, 2015 : 2:00 PM to 3:50 PM
• Sponsor: Section on Bayesian Statistical Science
• Author(s): David Hitchcock* and Wen Cheng and Ian Dryden and Huiling Le
• Companies: University of South Carolina and Wells Fargo and University of Nottingham and University of Nottingham
• Keywords: ambient space ; Fisher-Rao ; Gibbs sampler ; quotient space ; registration ; warp

Abstract

A Bayesian approach to function alignment is introduced. A model is proposed in the ambient space, with a Dirichlet prior for the derivative of the warping function and a Gaussian process for the square root velocity function. Posterior inference is carried out via Markov chain Monte Carlo simulation. The methodology is applied to a data set of mass spectrometry scans. Good alignment is obtained for most of the known proteins, with more uncertainty at either end of each scan.