DNA microarray profiles are plagued by the issue of large number of variables but small number of samples and are often notorious for their low signal-to-noise ratio for clinical applications. Therefore, a great need for meta-analysis techniques is emerging to yield more valid and informative results than each experiment separately. By exploring the power of several studies in one single analysis, meta-analysis of many cancer gene-profiling data increases the statistical power to detect differentially expressed genes and allows assessment of heterogeneity.OrderedListis such a method that was specially proposed for cancer gene expression data meta-analysis. It is superior to other methods in that it does not rely on strong effects of differential gene expression in a single study but on consistent regulated genes across multiple studies. This chapter introduces the R implementation of this methodology on real data sets to identify biomarkers for adenocarcinoma lung cancer.
