GOaGO
Gene Ontology enrichment analysis of gene pairs
Bioconductor version: 3.24 · Package version: 1.1.1
Other Bioconductor versions
devel is the development version; release is the current stable one.
3.24 (devel), 3.23 (release)
GO-a-GO annotates Gene Ontology terms that are enriched in a given set of gene pairs. The enrichment is calculated from a permutation test for overrepresentation of gene pairs that are associated with a shared term. Such gene pairs are counted for the original set of gene pairs and compared against randomized sets in which the structure of the pairs is preserved, but the gene identities (including the associated terms) are permuted.
Installation
if (!require("BiocManager", quietly = TRUE))
install.packages("BiocManager")
BiocManager::install("GOaGO")Details
| Maintainer | Aleksander Jankowski <aleksander.jankowski@uw.edu.pl> |
| Author | Aleksander Jankowski [aut, cre] (ORCID: <https://orcid.org/0000-0002-2212-6224>) |
| License | Artistic-2.0 |
| URL | https://github.com/ajank/GOaGO |
| Bug Reports | https://github.com/ajank/GOaGO/issues |
| Source branch | devel |
| Build report | Bioconductor build system, r-universe |
| biocViews | GO, GeneSetEnrichment, Software |
| Package Short Url | https://bioconductor.org/packages/GOaGO/ |
Citation
From within R, enter citation("GOaGO"):
Aleksander Jankowski. GOaGO: Gene Ontology enrichment analysis of gene pairs. doi:10.18129/B9.bioc.GOaGO, R package version 1.1.1, https://bioconductor.org/packages/GOaGO.
Generated from the package metadata; it may differ from the package's own citation.
Documentation
Download
Follow the installation instructions to use this package in your R session.
| Source package | GOaGO_1.1.1.tar.gz |
| Windows binary (x86_64) | GOaGO_1.1.1.zip |
| macOS binary (arm64) | GOaGO_1.1.1.tgz |
| macOS binary (x86_64) | GOaGO_1.1.1.tgz |
Dependencies
Depends: R (>= 4.4.0), methods
Imports: AnnotationDbi, BiocGenerics, BiocParallel, clusterProfiler, data.table, DOSE, GenomeInfoDb, GenomicRanges, ggplot2, ggridges, Matrix, qvalue, S4Vectors
Suggests: BiocStyle, GenomicInteractions, ggrepel, knitr, org.Hs.eg.db, rmarkdown, rtracklayer, testthat, TxDb.Hsapiens.UCSC.hg19.knownGene