gCMAP

DOI: 10.18129/B9.bioc.gCMAP    

This package is deprecated. It will probably be removed from Bioconductor. Please refer to the package end-of-life guidelines for more information.

This package is for version 3.11 of Bioconductor. This package has been removed from Bioconductor. For the last stable, up-to-date release version, see gCMAPWeb.

Tools for Connectivity Map-like analyses

Bioconductor version: 3.11

The gCMAP package provides a toolkit for comparing differential gene expression profiles through gene set enrichment analysis. Starting from normalized microarray or RNA-seq gene expression values (stored in lists of ExpressionSet and CountDataSet objects) the package performs differential expression analysis using the limma or DESeq packages. Supplying a simple list of gene identifiers, global differential expression profiles or data from complete experiments as input, users can use a unified set of several well-known gene set enrichment analysis methods to retrieve experiments with similar changes in gene expression. To take into account the directionality of gene expression changes, gCMAPQuery introduces the SignedGeneSet class, directly extending GeneSet from the GSEABase package. To increase performance of large queries, multiple gene sets are stored as sparse incidence matrices within CMAPCollection eSets. gCMAP offers implementations of 1. Fisher's exact test (Fisher, J R Stat Soc, 1922) 2. The "connectivity map" method (Lamb et al, Science, 2006) 3. Parametric and non-parametric t-statistic summaries (Jiang & Gentleman, Bioinformatics, 2007) and 4. Wilcoxon / Mann-Whitney rank sum statistics (Wilcoxon, Biometrics Bulletin, 1945) as well as wrappers for the 5. camera (Wu & Smyth, Nucleic Acid Res, 2012) 6. mroast and romer (Wu et al, Bioinformatics, 2010) functions from the limma package and 7. wraps the gsea method from the mgsa package (Bauer et al, NAR, 2010). All methods return CMAPResult objects, an S4 class inheriting from AnnotatedDataFrame, containing enrichment statistics as well as annotation data and providing simple high-level summary plots.

Author: Thomas Sandmann <sandmann.t at gmail.com>, Richard Bourgon <bourgon.richard at gene.com> and Sarah Kummerfeld <kummerfeld.sarah at gene.com>

Maintainer: Thomas Sandmann <sandmann.t at gmail.com>

Installation

To install this package, start R (version "4.0") and enter:

if (!requireNamespace("BiocManager", quietly = TRUE))
    install.packages("BiocManager")

BiocManager::install("gCMAP")

For older versions of R, please refer to the appropriate Bioconductor release.

Documentation

To view documentation for the version of this package installed in your system, start R and enter:

browseVignettes("gCMAP")

 

PDF	R Script	Creating reference datasets
PDF	R Script	gCMAP classes and methods
PDF		Reference Manual
Text		NEWS

Details

biocViews	Annotation, Microarray, Pathways, Software
Version	1.32.0
In Bioconductor since	BioC 2.11 (R-2.15) (8 years)
License	Artistic-2.0
Depends	GSEABase, limma(>= 3.20.0)
Imports	Biobase, methods, GSEAlm, Category, Matrix (>= 1.0.9), parallel, annotate, genefilter, AnnotationDbi, DESeq, grDevices, graphics, stats, utils, bigmemory, bigmemoryExtras(>= 1.1.2)
LinkingTo
Suggests	BiocGenerics, reactome.db, RUnit, GO.db, mgsa
SystemRequirements
Enhances
URL
Depends On Me	gCMAPWeb
Imports Me
Suggests Me
Links To Me
Build Report

Package Archives

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

Source Package	gCMAP_1.32.0.tar.gz
Windows Binary
macOS 10.13 (High Sierra)	gCMAP_1.32.0.tgz
Source Repository	git clone https://git.bioconductor.org/packages/gCMAP
Source Repository (Developer Access)	git clone git@git.bioconductor.org:packages/gCMAP
Package Short Url	https://bioconductor.org/packages/gCMAP/
Package Downloads Report	Download Stats