Key aspects of analyzing microarray gene-expression data.
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.
Chen
James J
JJ
US FDA, Division of Personalized Nutrition and Medicine, National Center for Toxicological Research, Jefferson, AR 72079, USA. jamesj.chen@fda.hhs.gov
D015870:Gene Expression; D020869:Gene Expression Profiling / Q000379:methods* / Q000706:statistics & numerical data / Q000639:trends; D008957:Models, Genetic; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods* / Q000706:statistics & numerical data / Q000639:trends; D011237:Predictive Value of Tests