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DESCRIPTION 100644 2 kb
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# gage R package [![](]( [![](]( [![](]( [![](]( ## 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=>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. `` 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]( Please check [SBGNview + GAGE based pathway analysis workflow]( ``` r #preparation library(gage) data(gse16873) hn=(1:6)*2-1 dcis=(1:6)*2 #KEGG pathway analysis data( gse16873.kegg.p <- gage(gse16873, gsets =, ref = hn, samp = dcis) #alternatively, you can also generate update KEGG gene sets: kg.hsa <- kegg.gsets("hsa") names(kg.hsa) <- 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$BP], ref = hn, samp = dcis) <- gage(gse16873, gsets = go.sets.hs[go.subs.hs$MF], ref = hn, samp = dcis) <- gage(gse16873, gsets = go.sets.hs[go.subs.hs$CC], ref = hn, samp = dcis) ``` ## More information Please check the <a href=>BioC page</a> for tutorials and extra documentations. Thank you for your interest.