Independent component analysis of 2-D electrophoresis gels
We present a novel application of independent component analysis (ICA), an exploratory data analysis technique, to two-dimensional electrophoresis (2-DE) gels, which have been used to analyze differentially expressed proteins across groups. Unlike currently used pixel-wise statistical tests, ICA is a data-driven approach that utilizes the information contained in the entire gel data. We also apply ICA on wavelet-transformed 2-DE gels to address the high dimensionality and noise problems typically found in 2-DE gels. Also, we use an analysis-of-variance (ANOVA) approach as a benchmark for comparison. Using simulated data, we show that ICA detects the group differences accurately in both the spatial and wavelet domains. We also apply these techniques to real 2-DE gels. ICA proves to be much faster than ANOVA, and unlike ANOVA it does not depend on the selection of a threshold. Application of principal component analysis reduces the dimensionality and tends to improve the performance by reducing the noise.