Wavelet-based noise reduction by joint statistical modeling of cdna microarray images

Complementary DNA (cDNA) microarray experiments involve a large number of error-prone steps, which result in a high level of noise in the resulting red and green channel images. Removal of noise is a crucial step, since it makes further image processing easier and results in accurate gene expression measurements. The wavelet transform has shown significant success in the denoising of images including cDNA microarrays. Existing wavelet-based denoising methods process each image individually ignoring the information in the other channel. In this paper, a noise reduction technique is proposed that exploits the dependency between the wavelet transform coefficients of the two channels by using a locally-adaptive joint statistical model. The maximum a posteriori criterion is used to derive a joint estimator for the noise-free coefficients assuming suitable priors for the local variances. Significance of the proposed method is assessed by examining its effect on estimation of the logintensity ratio. Experiments show that the proposed method provides an improved noise reduction performance in terms of mean squared error and yields log-intensity ratios that are close to the true values as compared to that of the standard denoising methods. AMS Subject Classification: 68U10; 62H12; 60G35. © 2009 Taylor & Francis Group, LLC. All rights reserved.