Abstract
Microarrays were a trigger to develop new methods which can allow to estimate disturbances in signal cascades, characterized by sets of genes, in various biological conditions. Existing approaches of gene set analysis take information if genes are differentially expressed or are based on some gene ranking. The most commonly used method is Gene Set Enrichment Analysis (GSEA), where an assumption of uniform distribution of genes in some gene set is tested by weighted Kolmogorov-Smirnov test. Many studies present different gene set analysis methods and their comparison, however none of them focus on basic but crucial parameters, like the rank metric. In this paper we compare nine ranking metrics in terms of sensitivity, specificity and prioritization of identification of functional gene sets using a collection of 34 annotated microarray datasets. We show that absolute value of default GSEA measure is the best ranking metric, while the Baumgartner-Weiss-Schindler test statistic is the best statistical-based metrics, which can be used in Gene Set Enrichment Analysis.
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Zyla, J., Marczyk, M., Polanska, J. (2016). Sensitivity, Specificity and Prioritization of Gene Set Analysis When Applying Different Ranking Metrics. In: Saberi Mohamad, M., Rocha, M., Fdez-Riverola, F., DomÃnguez Mayo, F., De Paz, J. (eds) 10th International Conference on Practical Applications of Computational Biology & Bioinformatics. PACBB 2016. Advances in Intelligent Systems and Computing, vol 477. Springer, Cham. https://doi.org/10.1007/978-3-319-40126-3_7
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DOI: https://doi.org/10.1007/978-3-319-40126-3_7
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