Bayesian inference for 2D gel electrophoresis image analysis

Two-dimensional gel electrophoresis (2DGE) is a technique to separate individual proteins in biological samples. The 2DGE technique results in gel images where proteins appear as dark spots on a white background. However, the analysis and inference of these images get complicated due to 1) contamination of gels, 2) superposition of proteins, 3) noisy background, and 4) weak protein spots. Therefore there is a strong need for an automatic analysis technique that is fast, robust, objective, and automatic to find protein spots. In this paper, to find protein spots more accurately and reliably from gel images, we propose Reversible Jump Markov Chain Monte Carlo method (RJMCMC) to search for underlying spots which are assume to have Gaussian-distribution shape. Our statistical method identifies very weak spots, restores noisy spots, and separates mixed spots into several meaningful spots which are likely to be ignored and missed. Our proposed approach estimates the proper number, centreposition, width, and amplitude of the spots and has been successfully applied to the field of projection reconstruction NMR (PR-NMR) processing [15,16]. To obtain a 2DGE image, we peformed 2DGE on the purified mitochondiral protein of liver from an adult Sprague-Dawley rat.