A visual analytics framework for cluster analysis of DNA microarray data

Cluster analysis of DNA microarray data is an important but difficult task in knowledge discovery processes. Many clustering methods are applied to analysis of data for gene expression, but none of them is able to deal with an absolute way with the challenges that this technology raises. Due to this, many applications have been developed for visually representing clustering algorithm results on DNA microarray data, usually providing dendrogram and heat map visualizations. Most of these applications focus only on the above visualizations, and do not offer further visualization components to the validate the clustering methods or to validate one another. This paper proposes using a visual analytics framework in cluster analysis of gene expression data. Additionally, it presents a new method for finding cluster boundaries based on properties of metric spaces. Our approach presents a set of visualization components able to interact with each other; namely, parallel coordinates, cluster boundary genes, 3D cluster surfaces and DNA microarray visualizations as heat maps. Experimental results have shown that our framework can be very useful in the process of more fully understanding DNA microarray data. The software has been implemented in Java, and the framework is publicly available at http://www.analiticavisual.com/jcastellanos/3DVisualCluster/3D-VisualCluster.

► We have introduced a framework of visual cluster analysis based on metric spaces.
► We have defined an algorithm to compute cluster boundary gene-points.
► We have defined an algorithm to reconstruct the 3D-surface of gene clusters.
► We have developed a tool of cluster analysis called 3D-VisualCluster.
► We have given a methodology by defining tasks to visually analyse the results of the clustering methods from DNA microarrays.