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To install this package, start R and enter:
source("http://bioconductor.org/biocLite.R")
biocLite("PathNet")
In most cases, you don't need to download the package archive at all.
PathNet
An R package for pathway analysis using topological information
Bioconductor version: 2.14
PathNet uses topological information present in pathways and differential expression levels of genes (obtained from microarray experiment) to identify pathways that are 1) significantly enriched and 2) associated with each other in the context of differential expression. The algorithm is described in: PathNet: A tool for pathway analysis using topological information. Dutta B, Wallqvist A, and Reifman J. Source Code for Biology and Medicine 2012 Sep 24;7(1):10.
Author: Bhaskar Dutta <bhaskar.dutta at gmail.com>, Anders Wallqvist <awallqvist at bhsai.org>, and Jaques Reifman <jreifman at bhsai.org>
Maintainer: Jason B. Smith <jsmith at bhsai.org>
Citation (from within R,
enter citation("PathNet")):
Installation
To install this package, start R and enter:
source("http://bioconductor.org/biocLite.R")
biocLite("PathNet")
Documentation
To view documentation for the version of this package installed in your system, start R and enter:
browseVignettes("PathNet")
| R Script | PathNet | |
| Reference Manual |
Details
| biocViews | DifferentialExpression, MultipleComparison, Pathways, Software |
| Version | 1.4.0 |
| In Bioconductor since | BioC 2.12 (R-3.0) |
| License | GPL-3 |
| Depends | R (>= 1.14.0) |
| Imports | |
| Suggests | PathNetData, RUnit, BiocGenerics |
| 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 | PathNet_1.4.0.tar.gz |
| Windows Binary | PathNet_1.4.0.zip (32- & 64-bit) |
| Mac OS X 10.6 (Snow Leopard) | PathNet_1.4.0.tgz |
| Mac OS X 10.9 (Mavericks) | PathNet_1.4.0.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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