Role of Permutations in Significance Analysis of Microarray and Clustering of Significant Microarray Gene list

Microarray is the gene expression data that represent gene in different biological states. Methods are needed to determine the significance of these changes while accounting for the enormous number of genes. Significance analysis of microarrays (SAM) is a statistical technique for determining whether changes in gene expression are statistically significant. During the SAM procedure permutation of microarray data is considered to observe the changes in the overall expression level of data. With increasing number of permutations false discovery rate for gene set varies. In our work we took microarray data of Normal Glucose Tolerance (NGT), and Diabetes Mellitus (DM Type II). In this paper we proposed the result of permutations during execution of SAM algorithm. The hierarchical clustering is applied for observing expression levels of significant data and visualize it with heat map.