To install this package, start R and enter:
source("http://bioconductor.org/biocLite.R")
biocLite("DART")
In most cases, you don't need to download the package archive at all.
DART
Denoising Algorithm based on Relevance network Topology
Bioconductor version: 3.0
Denoising Algorithm based on Relevance network Topology (DART) is an algorithm designed to evaluate the consistency of prior information molecular signatures (e.g in-vitro perturbation expression signatures) in independent molecular data (e.g gene expression data sets). If consistent, a pruning network strategy is then used to infer the activation status of the molecular signature in individual samples.
Author: Yan Jiao, Katherine Lawler, Andrew E Teschendorff
Maintainer: Katherine Lawler <katherine.lawler at kcl.ac.uk>
Citation (from within R,
enter citation("DART")):
Installation
To install this package, start R and enter:
source("http://bioconductor.org/biocLite.R")
biocLite("DART")
Documentation
To view documentation for the version of this package installed in your system, start R and enter:
browseVignettes("DART")
| R Script | DART Tutorial | |
| Reference Manual | ||
| Text | NEWS |
Details
| biocViews | DifferentialExpression, GeneExpression, GraphAndNetwork, Pathways, Software |
| Version | 1.12.0 |
| In Bioconductor since | BioC 2.10 (R-2.15) |
| License | GPL-2 |
| Depends | R (>= 2.10.0), igraph (>= 0.6.0) |
| Imports | |
| LinkingTo | |
| Suggests | breastCancerVDX, breastCancerMAINZ, Biobase |
| 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.
| Package Source | DART_1.12.0.tar.gz |
| Windows Binary | DART_1.12.0.zip |
| Mac OS X 10.6 (Snow Leopard) | DART_1.12.0.tgz |
| Mac OS X 10.9 (Mavericks) | DART_1.12.0.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
- 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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