DNA microarrays have demonstrated an excellent potential in correlating specific gene expression profiles to specific conditions. However, they are affected by inherent noise. This paper presents a two-stage approach for noise removal that processes the additive and the multiplicative noise component. The proposed approach first decomposes the signal by a multiresolution transform and then accounts for both the multiscale correlation of the subband decompositions and their heavy-tailed statistics. Real microarray images have been processed by the proposed method and its improved performance is shown through quantitative measures and qualitative visual evaluation.

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Distance-preserving dimension reduction techniques can fail to separate elements of different classes when the neighborhood structure does not carry sufficient class information. We introduce a new visual technique, K-epsilon diagrams, to analyze dataset topological structure and to assess whether intra-class and inter-class neighborhoods can be distinguished.We propose a force feature space data transform that emphasizes similarities between same-class points and enhances class separability. We show that the force feature space transform combined with distance-preserving dimension reduction produces better visualizations than dimension reduction alone. When used for classification, force feature spaces improve performance of Knearest neighbor classifiers. Furthermore, the quality of force feature space transformations can be assessed using K-epsilon diagrams.

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