Statistical analysis of cDNA microarray data for sample clustering and gene identification

Design/methodology/approach - The method relies on alternation of identification of the active genes using a mixture model and clustering of the samples based on Ward hierarchical clustering. The initial-point of the procedure is obtained by means of a ?2 test. The method attempts to locally minimize the sum of the within cluster sample variances under a suitable Gaussian assumption on the distribution of data. Findings - This paper illustrates the proposed methodology and its success by means of results from both simulated and real cDNA microarray data. The comparison of the results with those from a related known method demonstrates the superiority of the proposed approach. Research limitations/implications - Only empirical evidence of algorithm convergence is provided. Theoretical proof of algorithm convergence is an open issue. Practical implications - The proposed methodology can be applied to perform cDNA microarray data analysis. Originality/value - This paper provides a contribution to the development of successful statistical methods for cDNA microarray data analysis.