Analysis of Microarray Data for Identification Differentially Expressed Genes: A Survey

Abstract

A huge variety of clustering algorithms have been developed for analysis and differentially expressed genes Identification obtained from microarray experiment. The availability of different microarray types and growing variety of samples forming microarray studies cause new challenges within the analysis of microarray data. The existing clustering algorithms used for identification of differentially expressed genes are not promising as they do not take care of different conditions in micro array data. To overcome these limitations bi-clustering algorithms are used for analysis of micro array data to find subgroup of genes. In this paper, a comprehensive survey on analysis of data for identification of differentially expressed genes is presented. The paper also discusses about challenges involved in analysis of micro array data for identification of differentially expressed genes. The comparative analysis of various methods for differentially expressed genes identification is also reported. The future avenues of the research in the domain are also discussed.

Keywords: Micro Array Data, Differentially Expressed Genes, Clustering, Biclustering