To install this package, start R and enter:

## try http:// if https:// URLs are not supported
source("https://bioconductor.org/biocLite.R")
biocLite("goSTAG")

In most cases, you don't need to download the package archive at all.

goSTAG

DOI: 10.18129/B9.bioc.goSTAG    

A tool to use GO Subtrees to Tag and Annotate Genes within a set

Bioconductor version: Release (3.6)

Gene lists derived from the results of genomic analyses are rich in biological information. For instance, differentially expressed genes (DEGs) from a microarray or RNA-Seq analysis are related functionally in terms of their response to a treatment or condition. Gene lists can vary in size, up to several thousand genes, depending on the robustness of the perturbations or how widely different the conditions are biologically. Having a way to associate biological relatedness between hundreds and thousands of genes systematically is impractical by manually curating the annotation and function of each gene. Over-representation analysis (ORA) of genes was developed to identify biological themes. Given a Gene Ontology (GO) and an annotation of genes that indicate the categories each one fits into, significance of the over-representation of the genes within the ontological categories is determined by a Fisher's exact test or modeling according to a hypergeometric distribution. Comparing a small number of enriched biological categories for a few samples is manageable using Venn diagrams or other means for assessing overlaps. However, with hundreds of enriched categories and many samples, the comparisons are laborious. Furthermore, if there are enriched categories that are shared between samples, trying to represent a common theme across them is highly subjective. goSTAG uses GO subtrees to tag and annotate genes within a set. goSTAG visualizes the similarities between the over-representation of DEGs by clustering the p-values from the enrichment statistical tests and labels clusters with the GO term that has the most paths to the root within the subtree generated from all the GO terms in the cluster.

Author: Brian D. Bennett and Pierre R. Bushel

Maintainer: Brian D. Bennett <brian.bennett at nih.gov>

Citation (from within R, enter citation("goSTAG")):

Installation

To install this package, start R and enter:

## try http:// if https:// URLs are not supported
source("https://bioconductor.org/biocLite.R")
biocLite("goSTAG")

Documentation

HTML R Script The goSTAG User's Guide
PDF   Reference Manual
Text   NEWS

Details

biocViews Clustering, DifferentialExpression, GO, GeneExpression, GeneSetEnrichment, Microarray, RNASeq, Software, Visualization, mRNAMicroarray
Version 1.2.0
In Bioconductor since BioC 3.5 (R-3.4) (0.5 years)
License GPL-3
Depends R (>= 3.4)
Imports AnnotationDbi, biomaRt, GO.db, graphics, memoise, stats, utils
LinkingTo
Suggests BiocStyle, knitr, rmarkdown, testthat
SystemRequirements
Enhances
URL
Depends On Me
Imports Me
Suggests Me
Build Report  

Package Archives

Follow Installation instructions to use this package in your R session.

Source Package goSTAG_1.2.0.tar.gz
Windows Binary goSTAG_1.2.0.zip
Mac OS X 10.11 (El Capitan) goSTAG_1.2.0.tgz
Source Repository git clone https://git.bioconductor.org/packages/goSTAG
Package Short Url http://bioconductor.org/packages/goSTAG/
Package Downloads Report Download Stats

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