An Integrated Data Analysis Approach to Preprocessing, Visualization and Clustering of Microarray Data

Microarray technologies represent a powerful tool in biological research, but in order to attain their full potentialities, it is crucial to develop techniques to effectively exploit the huge quantity of data produced. We propose an innovative tool specifically tailored to perform preprocessing, visualization and clustering on this type of data. The improvements with respect to more traditional techniques are:(a) Preprocessing: a noise estimation method is developed to provide a formal, fast and accurate method to filter out the noisiest genes. The remaining genes are then processed using a nonlinear PCA model which extract, from each microarray experiment, a smaller number of features; (b) Visualization: the preprocessed genes represent the input of a Probabilistic Principal Surfaces (PPS), a latent variable model which has been shown to be very effective for data mining purposes; (c) Clustering: the trained PPS is used as bases for a clustering algorithm, based on a Negentropy information, able to compute, in an automatic way, the number of natural clusters inherently present in the data. All these phases are managed by means of a graphical user interface which provides further tools for data mining activities and gives the possibility to dynamically interact with the data. The tool was tested on yeast gene microarray data in order to find the groupings of co-regulated genes.