Abstract
Introducing the high-throughput measurement methods into molecular biology was a trigger to develop the algorithms for searching disorders in complex signalling systems, like pathways or gene ontologies. In recent years, there appeared many new solutions, but the results obtained with these techniques are ambiguous. In this work, five different algorithms for pathway enrichment analysis were compared using six microarray datasets covering cases with the same disease. The number of enriched pathways at different significance level and false positive rate of finding enrichment pathways was estimated, and reproducibility of obtained results between datasets was checked. The best performance was obtained for PLAGE method. However, taking into consideration the biological knowledge about analyzed disease condition, many findings may be false positives. Out of the other methods GSVA algorithm gave the most reproducible results across tested datasets, which was also validated in biological repositories. Similarly, good outcomes were given by GSEA method. ORA and PADOG gave poor sensitivity and reproducibility, which stand in contrary to previous research.
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Acknowledgements
This work was financed by SUT grant no. BKM/506/RAU1/2016/t.26 (JZ), 02/010/BK_16/3015 (MM) and NCN grant no. 2015/19/B/ST6/01736 (JP). All calculations were carried out using GeCONiI infrastructure funded by NCBiR project no. POIG.02.03.01-24-099/13.
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Zyla, J., Marczyk, M., Polanska, J. (2017). Reproducibility of Finding Enriched Gene Sets in Biological Data Analysis. In: Fdez-Riverola, F., Mohamad, M., Rocha, M., De Paz, J., Pinto, T. (eds) 11th International Conference on Practical Applications of Computational Biology & Bioinformatics. PACBB 2017. Advances in Intelligent Systems and Computing, vol 616. Springer, Cham. https://doi.org/10.1007/978-3-319-60816-7_18
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DOI: https://doi.org/10.1007/978-3-319-60816-7_18
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