cpvSNP
Gene set analysis methods for SNP association p-values that lie in genes in given gene sets
Bioconductor version: 3.24 · Package version: 1.45.0
Other Bioconductor versions
devel is the development version; release is the current stable one.
3.24 (devel), 3.23 (release)
Gene set analysis methods exist to combine SNP-level association p-values into gene sets, calculating a single association p-value for each gene set. This package implements two such methods that require only the calculated SNP p-values, the gene set(s) of interest, and a correlation matrix (if desired). One method (GLOSSI) requires independent SNPs and the other (VEGAS) can take into account correlation (LD) among the SNPs. Built-in plotting functions are available to help users visualize results.
Installation
if (!require("BiocManager", quietly = TRUE))
install.packages("BiocManager")
BiocManager::install("cpvSNP")Details
| Maintainer | Caitlin McHugh <mchughc@uw.edu> |
| Author | Caitlin McHugh, Jessica Larson, and Jason Hackney |
| License | Artistic-2.0 |
| Source branch | devel |
| Build report | Bioconductor build system, r-universe |
| biocViews | GeneSetEnrichment, Genetics, GenomicVariation, Pathways, Software, StatisticalMethod |
| Package Short Url | https://bioconductor.org/packages/cpvSNP/ |
Citation
From within R, enter citation("cpvSNP"):
Caitlin McHugh, Jessica Larson, and Jason Hackney. cpvSNP: Gene set analysis methods for SNP association p-values that lie in genes in given gene sets. doi:10.18129/B9.bioc.cpvSNP, R package version 1.45.0, https://bioconductor.org/packages/cpvSNP.
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 | cpvSNP_1.45.0.tar.gz |
| Windows binary (x86_64) | cpvSNP_1.45.0.zip |
| macOS binary (arm64) | cpvSNP_1.45.0.tgz |
| macOS binary (x86_64) | cpvSNP_1.45.0.tgz |
Dependencies
Depends: R (>= 3.5.0), GenomicFeatures, GSEABase (>= 1.24.0)
Imports: methods, corpcor, BiocParallel, ggplot2, plyr
Suggests: TxDb.Hsapiens.UCSC.hg19.knownGene, RUnit, BiocGenerics, ReportingTools, BiocStyle