Use of principal components analysis for mutation detection with two–dimensional electrophoresis protein separations

Abstract

The application of two-dimensional electrophoresis (2-DE) to mutation detection requires the capability to monitor each protein in a 2-DE pattern for significant changes in abundance indicative of a mutation event. Previously, mutation searches were done using a univariate outlier detection method in which each protein spot was considered independently in a classical outlier search. An alternative approach to analysis of 2-DE patterns for quantitative changes is a multivariate procedure which takes advantage of the observation that protein spots in a 2-DE pattern often represent correlated rather than independent measurements. We have compared the efficiency of univariate and multivariate procedures for mutation detection using data from the Argonne National Laboratory 2-DE database of mouse liver proteins. Analyses involving a total of over 1500 gels were performed to compare the performance of a multivariate method based on principal components analysis (PCA) with the univariate method. Up to 279 spots from each pattern were used for PCA. First, a simulation was performed to assess the detection efficiency of PCA for single protein spots decreased in abundance by 50%. Then, the ability to detect actual mutations was tested using eight confirmed mutations. Results show that, compared to a univariate approach to analysis of data from the mouse model system, the multivariate method increases the number of protein spots on each 2-DE pattern that can be monitored for quantitative changes indicative of mutations by compensating for variables that contribute to the background quantitative variability of protein spots.