doi: 10.17586/2226- 1494-2016-16-5-956-959


A. A. Sergushichev

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For citation: Sergushichev A.A. Algorithm for cumulative calculation of gene set enrichment statistic. Scientific and Technical Journal of Information Technologies, Mechanics and Optics, 2016, vol. 16, no. 5, pp. 956–959. doi: 10.17586/2226-1494-2016-16-5-956-959


Methods for gene set enrichment analysis, widely-used for analysis of gene expression data, were studied. A problem of cumulative calculation of enrichment statistic was considered. For this problem an algorithm based on square root decomposition heuristic was developed. An asymptotic run-time complexity of the algorithm was found. Practical implementation showed an order of magnitude increase in performance compared to a naïve algorithm when run on typical input sizes. The developed algorithm can be used to improve significantly the performance of gene set enrichment analysis.

Keywords: gene set enrichment analysis, gene expression, cumulative algorithm, empirical distribution, square root decomposition

Acknowledgements. This work was supported by the Russian Federation Government Grant No. 074-U01.


1. Mootha V.K., Lindgren C.M., Eriksson K.-F. et al. PGC-1 α-responsive genes involved in oxidative phosphorylation are coordinately downregulated in human diabetes. Nature Genetics, 2003, vol. 34, no. 3, pp. 267–273. doi: 10.1038/ng1180
2. Subramanian A., Tamayo P., Mootha V.K. et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proceedings of the National Academy of Sciences of the United States of America, 2005, vol. 102, no. 43, pp. 15545–15550. doi: 10.1073/pnas.0506580102
3. Maciejewski H. Gene set analysis methods: statistical models and methodological differences. Briefings in Bioinformatics, 2014, vol. 15, no. 4, pp. 504–518. doi: 10.1093/bib/bbt002
4. Tarca A.L., Bhatti G., Romero R. A comparison of gene set analysis methods in terms of sensitivity, prioritization and specificity. PloS ONE, 2013, vol. 8, no. 11, pp. e79217. doi: 10.1371/journal.pone.0079217
5. Yu G., Wang L.-G., Yan G.-R., He Q.-Y. DOSE: an R/Bioconductor package for disease ontology semantic and enrichment analysis. Bioinformatics, 2015, vol. 31, no. 4, pp. 608–609. doi: 10.1093/bioinformatics/btu684
6. Väremo L., Nielsen J., Nookaew I. Enriching the gene set analysis of genome-wide data by incorporating directionality of gene expression and combining statistical hypotheses and methods. Nucleic Acids Research, 2013, vol. 41, no. 8, pp. 4378¬–4391. doi: 10.1093/nar/gkt111
7. Fang Z. GSEAPY: Gene Set Enrichment Analysis in Python. Available at: (accessed 07.07.2016).
8. Ivabnov M. Sqrt-dekompozitsiya [Sqrt-decomposition]. Available at: (accessed 07.07.2016).
9. Cormen T.H., Leiserson C.E., Rivest R.L., Stein C. Introduction to Algorithms. 2nd ed. Cambridge, MIT Press, 2006, 1312 p.

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