Normalization of DNA-Microarray Data by Nonlinear Correlation Maximization

Abstract

Signal data from DNA-microarray ("chip") technology can be noisy; i.e., the signal variation of one gene on a series of repetitive chips can be substantial. It is becoming more and more recognized that a sufficient number of chip replicates has to be made in order to separate correct from incorrect signals. To reduce the systematic fraction of the noise deriving from pipetting errors, from different treatment of chips during hybridization, and from chip-to-chip manufacturing variability, normalization schemes are employed. We present here an iterative nonparametric nonlinear normalization scheme called simultaneous alternating conditional expectation (sACE), which is designed to maximize correlation between chip repeats in all-chip-against-all space. We tested sACE on 28 experiments with 158 Affymetrix one-color chips. The procedure should be equally applicable to other DNA-microarray technologies, e.g., two-color chips. We show that the reduction of noise compared to a simple normalization scheme like the widely used linear global normalization leads to fewer false-positive calls, i.e., to fewer genes which have to be laboriously confirmed by independent methods such as TaqMan or quantitative PCR.