This is the released version of GIGSEA; for the devel version, see GIGSEA.
Genotype Imputed Gene Set Enrichment AnalysisBioconductor version: Release (3.21)
We presented the Genotype-imputed Gene Set Enrichment Analysis (GIGSEA), a novel method that uses GWAS-and-eQTL-imputed trait-associated differential gene expression to interrogate gene set enrichment for the trait-associated SNPs. By incorporating eQTL from large gene expression studies, e.g. GTEx, GIGSEA appropriately addresses such challenges for SNP enrichment as gene size, gene boundary, SNP distal regulation, and multiple-marker regulation. The weighted linear regression model, taking as weights both imputation accuracy and model completeness, was used to perform the enrichment test, properly adjusting the bias due to redundancy in different gene sets. The permutation test, furthermore, is used to evaluate the significance of enrichment, whose efficiency can be largely elevated by expressing the computational intensive part in terms of large matrix operation. We have shown the appropriate type I error rates for GIGSEA (<5%), and the preliminary results also demonstrate its good performance to uncover the real signal.
Author: Shijia Zhu
Maintainer: Shijia Zhu <shijia.zhu at mssm.edu>
Citation (from within R, entercitation("GIGSEA")
): Installation
To install this package, start R (version "4.5") and enter:
if (!require("BiocManager", quietly = TRUE))
install.packages("BiocManager")
BiocManager::install("GIGSEA")
For older versions of R, please refer to the appropriate Bioconductor release.
DocumentationTo view documentation for the version of this package installed in your system, start R and enter:
browseVignettes("GIGSEA")
GIGSEA: Genotype Imputed Gene Set Enrichment Analysis PDF R Script Reference Manual PDF NEWS Text Details biocViews DifferentialExpression, GeneExpression, GeneRegulation, GeneSetEnrichment, Regression, SNP, Software, VariantAnnotation Version 1.26.0 In Bioconductor since BioC 3.8 (R-3.5) (6.5 years) License LGPL-3 Depends R (>= 3.5), Matrix, MASS, locfdr, stats, utils Imports System Requirements URL See More Package Archives
Follow Installation instructions to use this package in your R session.
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