BackgroundStatistical methods to tentatively identify differentially expressed genes in microarray studies typically assume larger sample sizes than are practical or even possible in some settings.ResultsThe performance of several probe-level and probeset models was assessed graphically and numerically using three spike-in datasets. Based on the Affymetrix GeneChip, a novel nested factorial model was developed and found to perform competitively on small-sample spike-in experiments.ConclusionsStatistical methods with test statistics related to the estimated log fold change tend to be more consistent in their performance on small-sample gene expression data. For such small-sample experiments, the nested factorial model can be a useful statistical tool. This method is implemented in freely-available R code (affyNFM), available with a tutorial document at http://www.stat.usu.edu/~jrstevens.

Gene Expression Analysis seeks to find the highly expressive genes from a highly dimensional Microarray disease gene Database by using some statistical gene selection approaches based on supervised or unsupervised learning. Gene Ontology (GO) introduces a series of method for annotating gene function that combines semantic similarity measures by taking account on the underlying topology of gene interaction networks for structuring the graphs of the gene ontology. Initially, the genes are identified by clustering microarray disease dataset giving gene id of most expressive genes and further the genes are associated based on their biological functionalities using the gene ontology annotations taken from bioinformatics database. Also, t-test is used for finding the up-regulated genes so it can be annotated to find the most significant gene terms in hierarchical graph structure. The proposed method uses term Similarity measures to compare two or more gene ontology terms. Finally, gene functional classification and gene term association is done by forming a graph structure to be readily analysed by medical practitioner intending the nature of disease-causing genes at deeper level of understanding in chronic disorder based health care environments.