edgeRun: an R package for sensitive, functionally relevant differential expression discovery using an unconditional exact test
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CitationDimont, Emmanuel, Jiantao Shi, Rory Kirchner, and Winston Hide. 2015. “edgeRun: an R package for sensitive, functionally relevant differential expression discovery using an unconditional exact test.” Bioinformatics 31 (15): 2589-2590. doi:10.1093/bioinformatics/btv209. http://dx.doi.org/10.1093/bioinformatics/btv209.
AbstractSummary: Next-generation sequencing platforms for measuring digital expression such as RNA-Seq are displacing traditional microarray-based methods in biological experiments. The detection of differentially expressed genes between groups of biological conditions has led to the development of numerous bioinformatics tools, but so far, few exploit the expanded dynamic range afforded by the new technologies. We present edgeRun, an R package that implements an unconditional exact test that is a more powerful version of the exact test in edgeR. This increase in power is especially pronounced for experiments with as few as two replicates per condition, for genes with low total expression and with large biological coefficient of variation. In comparison with a panel of other tools, edgeRun consistently captures functionally similar differentially expressed genes. Availability and implementation: The package is freely available under the MIT license from CRAN (http://cran.r-project.org/web/packages/edgeRun). Contact: firstname.lastname@example.org Supplementary information: Supplementary data are available at Bioinformatics online.
Citable link to this pagehttp://nrs.harvard.edu/urn-3:HUL.InstRepos:17820691