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Background: Gene set analysis is a valuable tool to summarize high-dimensional gene expression data in terms of biologically relevant sets. This is an active area of research and numerous gene set analysis methods have been developed. Despite this popularity, systematic comparative studies have been limited in scope. Methods: In this study we present a semi-synthetic simulation study using real datasets in order to test and compare commonly used methods. Results: A software pipeline, Flexible Algorithm for Novel Gene set Simulation (FANGS) develops simulated data based on a prostate cancer dataset where the KRAS and TGF-β pathways were differentially expressed. The FANGS software is compatible with other datasets and pathways. Comparisons of gene set analysis methods are presented for Gene Set Enrichment Analysis (GSEA), Significance Analysis of Function and Expression (SAFE), sigPathway, and Correlation Adjusted Mean RAnk (CAMERA) methods. All gene set analysis methods are tested using gene sets from the MSigDB knowledge base. The false positive rate and power are estimated and presented for comparison. Recommendations are made for the utility of the default settings of methods and each method's sensitivity towards various effect sizes. Conclusions: The results of this study provide empirical guidance to users of gene set analysis methods. The FANGS software is available for researchers for continued methods comparisons.
Mathur, R., Rotroff, D., Ma, J., Shojaie, A., & Motsinger-Reif, A. (2018). Gene set analysis methods: A systematic comparison. BioData Mining, 11(1). https://doi.org/10.1186/s13040-018-0166-8