On Determination of Minimum Sample Size for Discovery of Temporal Gene Expression Patterns

Abstract

DNA microarray technologies allow for the simultaneous monitoring of thousands of genes, which reveal important information about cellular and tissue expression phenotypes. From a viewpoint of data analysis, microarray experiments may be classified into (1) classification of patients or non-patients or more subtypes in terms of gene expressions, (2) discovery of gene expression patterns over a set of different conditions, and (3) discovery of gene expression patterns for one same tissue over a series of time points while the underlying biological process evolves. An important feature with this class of problems is dependency among gene expression data corresponding to time points. One of the important issues is the specification of time points, including (1) the number of time points, and (2) the span between time points. We have developed a method for the determination of the minimum sample size (or the minimum number of time points) for temporal gene expression, assuming that the span between time points is given and the hierarchical clustering technique is used for gene expression pattern discovery. The method has been verified with two previously published gene expression datasets; specifically for both experiments, the number of time points determined with our method is less than that in these experiments. Although at present our method employed the hierarchical clustering technique, the overall idea of the method is applicable to other clustering techniques