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Parry, R.M., Jones, W., Stokes, T.H., Phan, J.H., Moffitt, R.A., Fang, H., Shi, L., Oberthuer, A., Fischer, M., Tong, W. and Wang, M.D. (2010) K-nearest neighbor models for microarray gene expression analysis and clinical outcome prediction. Pharmacogenomics Journal, 10, 292-309.

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• TITLE: Microarray data analysis: Gaining biological insights

  AUTHORS: Rumdeep Kaur Grewal, Sampa Das

  KEYWORDS: Microarray; Cluster; Pathway; Network

  JOURNAL NAME: Journal of Biomedical Science and Engineering, Vol.6 No.10, October 28, 2013

  ABSTRACT: DNA microarray is a widely used technique which allows one to identify the genes that are similarly or differentially expressed in different cell types or conditions, to learn how their expression levels change in different developmental stages or disease states, and to identify the cellular processes in which they participate. This technology produces a large amount of complex data, necessitating employment of multiple bioinformatics and computational tools and techniques to provide a comprehensive view of the underlying biology. This review overviews methods and techniques which may be employed to analyze and interpret microarray data. The focus is primarily on analysis of gene expression matrices to obtain biological insights to this end. Both supervised and unsupervised methods commonly used for expression data analysis have been discussed. Data visualization techniques which may be used to comprehend biological relevance of the data has also been discussed in brief.