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To install this package, start R and enter:
source("http://bioconductor.org/biocLite.R")
biocLite("miRNApath")
In most cases, you don't need to download the package archive at all.
miRNApath
miRNApath: Pathway Enrichment for miRNA Expression Data
Bioconductor version: 2.14
This package provides pathway enrichment techniques for miRNA expression data. Specifically, the set of methods handles the many-to-many relationship between miRNAs and the multiple genes they are predicted to target (and thus affect.) It also handles the gene-to-pathway relationships separately. Both steps are designed to preserve the additive effects of miRNAs on genes, many miRNAs affecting one gene, one miRNA affecting multiple genes, or many miRNAs affecting many genes.
Author: James M. Ward <jmw86069 at gmail.com> with contributions from Yunling Shi, Cindy Richards, John P. Cogswell
Maintainer: James M. Ward <jmw86069 at gmail.com>
Citation (from within R,
enter citation("miRNApath")):
Installation
To install this package, start R and enter:
source("http://bioconductor.org/biocLite.R")
biocLite("miRNApath")
Documentation
To view documentation for the version of this package installed in your system, start R and enter:
browseVignettes("miRNApath")
| R Script | miRNApath: Pathway Enrichment for miRNA Expression Data | |
| Reference Manual | ||
| Text | NEWS |
Details
| biocViews | Annotation, DifferentialExpression, NetworkEnrichment, Pathways, Software, miRNA |
| Version | 1.24.0 |
| In Bioconductor since | BioC 2.3 (R-2.8) |
| License | LGPL-2.1 |
| Depends | methods, R (>= 2.7.0) |
| Imports | |
| Suggests | |
| System Requirements | |
| URL | |
| Depends On Me | |
| Imports Me | |
| Suggests Me |
Package Archives
Follow Installation instructions to use this package in your R session.
| Package Source | miRNApath_1.24.0.tar.gz |
| Windows Binary | miRNApath_1.24.0.zip (32- & 64-bit) |
| Mac OS X 10.6 (Snow Leopard) | miRNApath_1.24.0.tgz |
| Mac OS X 10.9 (Mavericks) | miRNApath_1.24.0.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
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