Spot Detection and Image Segmentation in DNA Microarray Data

Following the invention of microarrays in 1994, the development and applications of this technology have grown exponentially. The numerous applications of microarray technology include clinical diagnosis and treatment, drug design and discovery, tumour detection, and environmental health research. One of the key issues in the experimental approaches utilising microarrays is to extract quantitative information from the spots, which represent genes in a given experiment. For this process, the initial stages are important and they influence future steps in the analysis. Identifying the spots and separating the background from the foreground is a fundamental problem in DNA microarray data analysis. In this review, we present an overview of state-of-the-art methods for microarray image segmentation. We discuss the foundations of the circle-shaped approach, adaptive shape segmentation, histogram-based methods and the recently introduced clustering-based techniques. We analytically show that clustering-based techniques are equivalent to the one-dimensional, standard k-means clustering algorithm that utilises the Euclidean distance

DNA expression microarrays may be the wrong tool to identify biological pathways

DNA microarray expression signatures are expected to provide new insights into patho- physiological pathways. Numerous variant statistical methods have been described for each step of the signal analysis. We employed five similar statistical tests on the same data set at the level of gene selection. Inter-test agreement for the identification of biological pathways in BioCarta, KEGG and Reactome was calculated using Cohen’s k- score. The identification of specific biological pathways showed only moderate agreement (0.30 < k < 0.79) between the analysis methods used. Pathways identified by microarrays must be treated cautiously as they vary according to the statistical method used

Differential gene expression graphs: A data structure for classification in DNA microarrays

This paper proposes an innovative data structure to be used as a backbone in designing microarray phenotype sample classifiers. The data structure is based on graphs and it is built from a differential analysis of the expression levels of healthy and diseased tissue samples in a microarray dataset. The proposed data structure is built in such a way that, by construction, it shows a number of properties that are perfectly suited to address several problems like feature extraction, clustering, and classificatio