Robust Microarray Meta-Analysis Identifies Differentially Expressed Genes for Clinical Prediction

Figure 1

Study design diagram. We compare the predictive performance of meta-analysis-based feature selection (FS) methods by designing a study that considers five components: (1) basic FS methods that are the building blocks of some of the meta-analysis methods, (2) meta-analysis-based FS methods, (3) clinical application, (4) microarray data platform, and (5) classifier (logistic regression, diagonal LDA and linear SVM). Since the “best” meta-analysis-based FS method may be dataset- or application-specific, assessing performance over a wide variety of factors enables an evaluation of the method’s robustness.