Statistical analysis of C‐DNA microarray data for sample clustering and gene identification

Abstract

PurposeThis purpose of this paper is to describe a fast and easy method of both clustering samples and identifying active genes in cDNA microarray data.Design/methodology/approachThe 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 χ2 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.FindingsThis 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/implicationsOnly empirical evidence of algorithm convergence is provided. Theoretical proof of algorithm convergence is an open issue.Practical implicationsThe proposed methodology can be applied to perform cDNA microarray data analysis.Originality/valueThis paper provides a contribution to the development of successful statistical methods for cDNA microarray data analysis.