title: Independent component analysis of 2-D electrophoresis gels.
abstract: 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.
authors.affiliation: University of Maryland Baltimore County, Baltimore, MD, USA. haleh1@umbc.edu
authors.forename: Haleh
authors.initials: H
authors.lastname: Safavi
authors.forename: Nicolle
authors.initials: N
authors.lastname: Correa
authors.forename: Wei
authors.initials: W
authors.lastname: Xiong
authors.forename: Anindya
authors.initials: A
authors.lastname: Roy
authors.forename: Tülay
authors.initials: T
authors.lastname: Adali
authors.forename: Valeriy R
authors.initials: VR
authors.lastname: Korostyshevskiy
authors.forename: Carol C
authors.initials: CC
authors.lastname: Whisnant
authors.forename: Françoise
authors.initials: F
authors.lastname: Seillier-Moiseiwitsch
mesh_terms: D000465:Algorithms; D000704:Analysis of Variance; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D015180:Electrophoresis, Gel, Two-Dimensional; D016011:Normal Distribution; D025341:Principal Component Analysis