Microarray Image Denoising using Independent Component Analysis

Abstract

DNA microarrays have proved to be one of the vital breakthrough technologies for exploring the patterns of gene expression on a global scale. The differential measured gene-expression levels depend largely on the probe intensities extracted during microarray image processing. Various noises introduced during the experiment and the imaging process can drastically influence the accuracy of results. Microarray image denoising is one of the challenging preprocessing steps in microarray image analysis. In this paper, we propose denoising of microarray images using the independent component analysis (ICA). The idea of ICA i.e. finding the linear representation of nongaussian data so that the components are independent (or atleast as independent as possible) is exploited for denoising microarray images. Through examples, it is shown that the proposed approach is highly effective as compared to the conventional discrete wavelet transform and statistical methods.