An Evaluation of Gene Set Analysis for Biomarker Discovery with Applications to Myeloma Research

In this paper, we evaluate 15 methods for gene set analysis in microarray classification problems. We employ four datasets from myeloma research and three types of biological gene sets, encompassing a total of 12 scenarios. Taking a two-step approach, we first identify important genes within gene sets to create summary gene set scores, we then construct predictive models using the gene set scores as predictors. We propose two powerful linear methods in addition to the well-known SuperPC method for calculating scores. By comparing the 15 gene set methods with methods used in individual-gene analysis, we conclude that, overall, the gene set analysis approach provided more accurate predictions than the individual-gene analysis.