SOM based clustering of two-dimensional gel image segments for quantitative validation of changes in proteome

Abstract

In this paper, we focus on the quantitative comparison of two-dimensional gel images in order to indicate the possible changes of the proteome in separate samples. The comparison of two or more separate gel images cannot be straightforward because it is hard to ensure the same experimental conditions for each experiment. The aim of our investigation was to increase the precision of quantitative validation of changes in proteome of analyzed biological tissue. Our work was divided into two parts: analysis of uncertainties during quantification of gel images prepared with different staining intensities, images with saturated protein spots and to propose an image processing technique to minimize these uncertainties. We proposed a SOM based clustering of watershed transformation based image segmentation result and separate comparison of protein spot intensities in each group. The experimental investigation showed, that the effective SOM size is 3×3 and background subtraction based on pixel intensities on image segment contour give less uncertainty comparing to alternative techniques.