egsea.base: EGSEA Base GSE Methods

Description Usage Details Value References Examples

View source: R/egsea.R


It lists the supported GSEA methods. Since EGSEA base methods are implemented in the Bioconductor project, the most recent version of each individual method is always used.




These methods include: ora[1], globaltest[2], plage[3], safe[4], zscore[5], gage[6], ssgsea[7], roast[8], fry[8], padog[9], camera[10] and gsva[11]. The ora, gage, camera and gsva methods depend on a competitive null hypothesis while the remaining seven methods are based on a self-contained hypothesis. Conveniently, EGSEA is not limited to these twelve GSE methods and new GSE tests can be easily integrated into the framework.

Note: the execution time of base methods can vary depending on the size of gene set collections, number of samples, number of genes and number of contrasts. When a gene set collection of around 200 gene sets was tested on a dataset of 17,500 genes, 8 samples and 2 contrasts, the execution time of base methods in ascending order was as follows: globaltest; safe; gage; gsva; zscore; plage; fry; camera; ora; ssgsea; padog. When the same dataset was tested on a large gene set collection of 3,700 gene sets, the execution time of base methods in ascending order was as follows: globaltest; camera; fry; zscore; plage; safe; gsva; ora; gage; padog; ssgsea. Apparently, the size of gene set collection plays a key role in the execution time of most of the base methods. The reduction rate of execution time between the large and small gene set collections varied between 18% and 88%. camera, fry, plage, zscore and ora showed the least reduction rate of execution time. As a result, there is no guarantee that a single combination of base methods would run faster than other combinations. It is worth mentioning that our simulation results showed that the increasing number of base methods in the EGSEA analysis is desirable to achieve high performance.


It returns a character vector of supported GSE methods.


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[2] Goeman, J. J. et al. (2004). A global test for groups of genes: testing association with a clinical outcome. Bioinformatics, 20(1), 93-9.
[3] Tomfohr, J. et al. (2005). Pathway level analysis of gene expression using singular value decomposition. BMC Bioinformatics, 6, 225.
[4] Barry, W. T. et al. (2005). Significance analysis of functional categories in gene expression studies: a structured permutation approach. Bioinformatics, 21(9), 1943-9.
[5] Lee, E. et al. (2008). Inferring pathway activity toward precise disease classification. PLoS Computational Biology, 4(11), e1000217.
[6] Luo, W. et al. (2009). GAGE: generally applicable gene set enrichment for pathway analysis. BMC Bioinformatics, 10, 161.
[7] Barbie, D. A. et al. (2009). Systematic RNA interference reveals that oncogenic KRASdriven cancers require TBK1. Nature, 462(7269), 108-12.
[8] Wu, D. et al. (2010). ROAST: rotation gene set tests for complex microarray experiments. Bioinformatics, 26(17), 2176-82.
[9] Tarca, A. L. et al. (2009). A novel signaling pathway impact analysis. Bioinformatics, 25(1), 75-82.
[10] Wu, D. and Smyth, G. K. (2012). Camera: a competitive gene set test accounting for inter-gene correlation. Nucleic Acids Research, 40(17), e133.
[11] Hanzelmann, S. et al. (2013). GSVA: gene set variation analysis for microarray and RNA-seq data. BMC Bioinformatics, 14, 7.



EGSEA documentation built on July 21, 2017, 7:20 p.m.