Key Aspects of Analyzing Microarray Gene-Expression Data

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.

Keywords: :

• class comparison
• class prediction
• cross validation
• gene class testing
• gene selection
• multiple selection criteria
• multiple testing
• significance analysis

Disclaimer

The views presented in this paper are those of the author and do not necessarily represent those of the USA FDA. J Chen declares that there are no conflicts of interest.