Clustering gene expression data using a diffraction‐inspired framework

Steven C Dinger, Michael A Van Wyk, Sergio Carmona, David M Rubin

The recent developments in microarray technology has allowed for the simultaneous measurement of gene expression levels. The large amount of captured data challenges conventional statistical tools for analysing and finding inherent correlations between genes and samples. The unsupervised clustering approach is often used, resulting in the development of a wide variety of algorithms. Typical clustering algorithms require selecting certain parameters to operate, for instance the number of expected clusters, as well as defining a similarity measure to quantify the distance between data points. The diffraction-based clustering algorithm however is designed to overcome this necessity for user-defined parameters, as it is able to automatically search the data for any underlying structure.