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Statistical Analysis of DNA Microarray Data in Cancer Research

Authors' Affiliations: ¹ Statistics Lab, Department of Operations Research and Financial Engineering, Princeton University, Princeton, New Jersey and ² Center for Hematology and Oncology Molecular Therapeutics, Taussig Cancer Center, Cleveland Clinic, Cleveland, Ohio

Requests for reprints: Jianqing Fan, Department of Operations Research and Financial Engineering, Princeton University, Princeton, NJ 08540. Phone: 609-258-7924; Fax: 609-258-8115; E-mail: jqfan{at}princeton.edu.

Microarray techniques have been widely used to monitor gene expression in many areas of biomedical research. They have been widely used for tumor diagnosis and classification, prediction of prognoses and treatment, and understanding of molecular mechanisms, biochemical pathways, and gene networks. Statistical methods are vital for these scientific endeavors. This article reviews recent developments of statistical methods for analyzing data from microarray experiments. Emphasis has been given to normalization of expression from multiple arrays, selecting significantly differentially expressed genes, tumor classifications, and gene expression pathways and networks.

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