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Use of principal components analysis for mutation detection with two–dimensional electrophoresis protein separations
Author(s) -
Taylor John,
Giometti Carol S.
Publication year - 1992
Publication title -
electrophoresis
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.666
H-Index - 158
eISSN - 1522-2683
pISSN - 0173-0835
DOI - 10.1002/elps.1150130133
Subject(s) - univariate , principal component analysis , multivariate statistics , outlier , multivariate analysis , spots , anomaly detection , mutation , biology , pattern recognition (psychology) , computational biology , computer science , artificial intelligence , statistics , genetics , mathematics , gene , botany
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.