# gage R package
[](https://www.bioconductor.org/packages/gage)
[](https://github.com/datapplab/gage)
[](https://www.bioconductor.org/packages/gage)
[](https://github.com/datapplab/gage)
## Overview
GAGE is a widely used method for gene set (enrichment or GSEA) or pathway analysis. GAGE is generally applicable independent of microarray or RNA-Seq data attributes including sample sizes, experimental designs, assay platforms, and other types of heterogeneity, and consistently achieves superior performance over other frequently used methods.
## Citation
Please cite the GAGE paper when using this open-source package. This will help the project and our team:
Luo W, Friedman M, etc. GAGE: generally applicable gene set enrichment for pathway analysis. BMC Bioinformatics, 2009, 10, pp. 161, <a href=https://doi.org/10.1186/1471-2105-10-161>doi: 10.1186/1471-2105-10-161</a>
## Installation (within R)
``` r
# install from BioConductor
if(!requireNamespace("BiocManager", quietly = TRUE))
install.packages("BiocManager")
BiocManager::install("gage")
# Or the development version from GitHub:
# install.packages("devtools")
devtools::install_github("datapplab/gage")
```
## Quick start with demo data (R code)
Note we use the demo gene set data, i.e. `kegg.gs` and `go.sets.hs`. You can generate up-to-date gene set data using `kegg.gsets` and `go.gsets` functions. Please check the help info on the function for details. Also here we focuse on KEGG pathways, which is good for most regular analyses. If you are interested in working with other major pathway databases, including Reactome, MetaCyc, SMPDB, PANTHER, METACROP etc, you can use [SBGNview](https://github.com/datapplab/SBGNview). Please check [SBGNview + GAGE based pathway analysis workflow](https://bioconductor.org/packages/devel/bioc/vignettes/SBGNview/inst/doc/pathway.enrichment.analysis.html).
``` r
#preparation
library(gage)
data(gse16873)
hn=(1:6)*2-1
dcis=(1:6)*2
#KEGG pathway analysis
data(kegg.gs)
gse16873.kegg.p <- gage(gse16873, gsets = kegg.gs, ref = hn, samp = dcis)
#alternatively, you can also generate update KEGG gene sets:
kg.hsa <- kegg.gsets("hsa")
names(kg.hsa)
kegg.gs <- kg.hsa$kg.sets[kg.hsa$sigmet.idx]
#GO term analysis, separate BP, MF and CC categories, need to generate GO gene sets first
go.hs <- go.gsets(species="human")
names(go.hs)
go.sets.hs <- go.hs$go.sets
go.subs.hs <- go.hs$go.subs
gse16873.bp.p <- gage(gse16873, gsets = go.sets.hs[go.subs.hs$BP], ref = hn, samp = dcis)
gse16873.mf.p <- gage(gse16873, gsets = go.sets.hs[go.subs.hs$MF], ref = hn, samp = dcis)
gse16873.cc.p <- gage(gse16873, gsets = go.sets.hs[go.subs.hs$CC], ref = hn, samp = dcis)
```
## More information
Please check the <a href=https://bioconductor.org/packages/gage/>BioC page</a> for tutorials and extra documentations. Thank you for your interest.
