A clustering method based on recursive bisection is introduced for analyzing microarray gene expression data. Either or both dimensions for the genes and the samples of a given microarray dataset can be classified in an unsupervised fashion. Alternatively, if certain prior knowledge of the genes or samples is available, a supervised version of the clustering analysis can also be carried out. Either approach may be used to generate a partial or complete binary hierarchy, the dendrogram, showing the underlying structure of the dataset. Compared to other existing clustering methods used for microarray data analysis (such as hierarchical and K-means), the method presented here has the advantage of much improved computational efficiency while retaining effective separation of data clusters under a distance metric, a straightforward parallel implementation, and useful extraction and presentation of biological information. Clustering results of both synthesized and experimental microarray data are presented to demonstrate the performance of the algorithm.
['Ruye Wang', 'Lucas Scharenbroich', 'Christopher E. Hart', 'Barbara J. Wold', 'Eric Mjolsness']
Clustering analysis of microarray gene expression data by splitting algorithm