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.
Safavi, H., Correa, N., Xiong, W., Roy, A., Adali, T., Korostyshevskiy, VR., ... Sellier-Moiseiwitsch, F. (2008). Independent component analysis of 2-D electrophoresis gels. Electrophoresis, 29(19), 4017-4026. https://doi.org/10.1002/elps.200800028