Automatic protein Spots Quantification in Two-Dimensional gel Images

Two-dimensional (2D) polyacrylamide gel electrophoresis of proteins is a robust and reproducible technique. It is the most widely used separation tool in proteomics. Current efforts in the field are directed at development of tools for expanding the range of proteins accessible with 2D gels. Proteomics was built around the 2D gel. The idea that multiple proteins can be analyzed in parallel grew from 2D gel maps. Proteomics researchers needed to identify interested protein spots by examining the gel. This is time-consuming, labor-extensive, and error-prone process. It is desired that the computer can analyze the proteins automatically by first detecting then quantifying the protein spots in the 2D gel images. In our previous work, we presented a new technique for segmentation of 2D gel images using the fuzzy c-means (FCM) algorithm using the notion of fuzzy relations. In this paper, we will describe the new relational FCM (RFCM) algorithm and use it for automatic protein spots quantification. We will also use two methods to evaluate its performance: the unsupervised evaluation method and comparison with the expert spots quantification.