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Pharmacogenomics. 2007 May;8(5):473-82.

Key aspects of analyzing microarray gene-expression data.

Author information

  • 1US FDA, Division of Personalized Nutrition and Medicine, National Center for Toxicological Research, Jefferson, AR 72079, USA. jamesj.chen@fda.hhs.gov

Abstract

One major challenge with the use of microarray technology is the analysis of massive amounts of gene-expression data for various applications. This review addresses the key aspects of the microarray gene-expression data analysis for the two most common objectives: class comparison and class prediction. Class comparison mainly aims to select which genes are differentially expressed across experimental conditions. Gene selection is separated into two steps: gene ranking and assigning a significance level. Class prediction uses expression profiling analysis to develop a prediction model for patient selection, diagnostic prediction or prognostic classification. Development of a prediction model involves two components: model building and performance assessment. It also describes two additional data analysis methods: gene-class testing and multiple ordering criteria.

PMID:
17465711
[PubMed - indexed for MEDLINE]
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