To install this package, start R and enter:
source("http://bioconductor.org/biocLite.R")
biocLite("mgsa")
In most cases, you don't need to download the package archive at all.
mgsa
Model-based gene set analysis
Bioconductor version: 3.0
Model-based Gene Set Analysis (MGSA) is a Bayesian modeling approach for gene set enrichment. The package mgsa implements MGSA and tools to use MGSA together with the Gene Ontology.
Author: Sebastian Bauer <mail at sebastianbauer.info>, Julien Gagneur <gagneur at genzentrum.lmu.de>
Maintainer: Sebastian Bauer <mail at sebastianbauer.info>
Citation (from within R,
enter citation("mgsa")):
Installation
To install this package, start R and enter:
source("http://bioconductor.org/biocLite.R")
biocLite("mgsa")
Documentation
To view documentation for the version of this package installed in your system, start R and enter:
browseVignettes("mgsa")
| R Script | Overview of the mgsa package. | |
| Reference Manual |
Details
| biocViews | GO, GeneSetEnrichment, Pathways, Software |
| Version | 1.14.2 |
| In Bioconductor since | BioC 2.8 (R-2.13) |
| License | Artistic-2.0 |
| Depends | R (>= 2.14.0), methods, gplots |
| Imports | graphics, stats, utils |
| LinkingTo | |
| Suggests | DBI, RSQLite, GO.db, testthat |
| SystemRequirements | |
| Enhances | |
| URL | |
| Depends On Me | |
| Imports Me | |
| Suggests Me | gCMAP |
| Build Report |
Package Archives
Follow Installation instructions to use this package in your R session.
| Package Source | mgsa_1.14.2.tar.gz |
| Windows Binary | mgsa_1.14.2.zip (32- & 64-bit) |
| Mac OS X 10.6 (Snow Leopard) | mgsa_1.14.2.tgz |
| Mac OS X 10.9 (Mavericks) | mgsa_1.14.2.tgz |
| Browse/checkout source | (username/password: readonly) |
| Package Downloads Report | Download Stats |
Common Bioconductor workflows include:
- Oligonucleotide Arrays
- High-throughput Sequencing
- Counting Reads for Differential Expression (parathyroideSE vignette)
- Annotation
- Annotating Variants
- Annotating Ranges
- Flow Cytometry and other assays
- Candidate Binding Sites for Known Transcription Factors
- Cloud-enabled cis-eQTL search and annotation
- RNA-Seq workflow: gene-level exploratory analysis and differential expression
- Changing genomic coordinate systems with rtracklayer::liftOver
- Mass spectrometry and proteomics data analysis
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