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gage

Generally Applicable Gene-set Enrichment for Pathway Analysis

https://github.com/datapplab/gage

Science Score: 13.0%

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Last synced: 11 months ago · JSON representation

Repository

Generally Applicable Gene-set Enrichment for Pathway Analysis

Basic Info
  • Host: GitHub
  • Owner: datapplab
  • Language: R
  • Default Branch: master
  • Size: 2.16 MB
Statistics
  • Stars: 5
  • Watchers: 1
  • Forks: 4
  • Open Issues: 5
  • Releases: 0
Created about 6 years ago · Last pushed almost 4 years ago
Metadata Files
Readme

README.md

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, doi: 10.1186/1471-2105-10-161

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. 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(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 BioC page for tutorials and extra documentations. Thank you for your interest.

Owner

  • Name: datapplab
  • Login: datapplab
  • Kind: organization

GitHub Events

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Last synced: about 2 years ago

All Time
  • Total Commits: 24
  • Total Committers: 2
  • Avg Commits per committer: 12.0
  • Development Distribution Score (DDS): 0.083
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  • Commits: 0
  • Committers: 0
  • Avg Commits per committer: 0.0
  • Development Distribution Score (DDS): 0.0
Top Committers
Name Email Commits
Weijun Luo l****n@y****m 22
weijun w****n@d****1 2

Issues and Pull Requests

Last synced: 11 months ago

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  • Total issues: 5
  • Total pull requests: 0
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  • Total issue authors: 5
  • Total pull request authors: 0
  • Average comments per issue: 0.8
  • Average comments per pull request: 0
  • Merged pull requests: 0
  • Bot issues: 0
  • Bot pull requests: 0
Past Year
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  • Pull requests: 0
  • Average time to close issues: N/A
  • Average time to close pull requests: N/A
  • Issue authors: 0
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  • Average comments per issue: 0
  • Average comments per pull request: 0
  • Merged pull requests: 0
  • Bot issues: 0
  • Bot pull requests: 0
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Top Authors
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  • lananhle (1)
  • marwa38 (1)
  • angiachino (1)
  • leorippel (1)
  • ArthurPERE (1)
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Packages

  • Total packages: 1
  • Total downloads:
    • bioconductor 194,066 total
  • Total dependent packages: 4
  • Total dependent repositories: 0
  • Total versions: 5
  • Total maintainers: 1
bioconductor.org: gage

Generally Applicable Gene-set Enrichment for Pathway Analysis

  • Versions: 5
  • Dependent Packages: 4
  • Dependent Repositories: 0
  • Downloads: 194,066 Total
Rankings
Dependent repos count: 0.0%
Dependent packages count: 0.0%
Average: 3.0%
Downloads: 9.1%
Maintainers (1)
luo_weijun@yahoo.com
Last synced: 11 months ago

Dependencies

DESCRIPTION cran
  • R >= 2.10 depends
  • AnnotationDbi * imports
  • GO.db * imports
  • KEGGREST * imports
  • graph * imports
  • DESeq2 * suggests
  • GSEABase * suggests
  • GenomicAlignments * suggests
  • Rsamtools * suggests
  • TxDb.Hsapiens.UCSC.hg19.knownGene * suggests
  • edgeR * suggests
  • gageData * suggests
  • hgu133a.db * suggests
  • limma * suggests
  • org.Hs.eg.db * suggests
  • pathview * suggests