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To install this package, start R and enter:
source("http://bioconductor.org/biocLite.R")
biocLite("ddgraph")
In most cases, you don't need to download the package archive at all.
ddgraph
Distinguish direct and indirect interactions with Graphical Modelling
Bioconductor version: 3.0
Distinguish direct from indirect interactions in gene regulation and infer combinatorial code from highly correlated variables such as transcription factor binding profiles. The package implements the Neighbourhood Consistent PC algorithm (NCPC) and draws Direct Dependence Graphs to represent dependence structure around a target variable. The package also provides a unified interface to other Graphical Modelling (Bayesian Network) packages for distinguishing direct and indirect interactions.
Author: Robert Stojnic
Maintainer: Robert Stojnic <robert.stojnic at gmail.com>
Citation (from within R,
enter citation("ddgraph")):
Installation
To install this package, start R and enter:
source("http://bioconductor.org/biocLite.R")
biocLite("ddgraph")
Documentation
To view documentation for the version of this package installed in your system, start R and enter:
browseVignettes("ddgraph")
| R Script | Overview of the 'ddgraph' package | |
| Reference Manual | ||
| Text | NEWS |
Details
| biocViews | GraphAndNetwork, Software |
| Version | 1.10.1 |
| In Bioconductor since | BioC 2.11 (R-2.15) |
| License | GPL-3 |
| Depends | graph, methods, Rcpp |
| Imports | bnlearn (>= 2.8), gtools, pcalg, RColorBrewer, plotrix, MASS |
| LinkingTo | Rcpp |
| Suggests | Rgraphviz, e1071, ROCR, testthat |
| 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 | ddgraph_1.10.1.tar.gz |
| Windows Binary | ddgraph_1.10.1.zip (32- & 64-bit) |
| Mac OS X 10.6 (Snow Leopard) | ddgraph_1.10.1.tgz |
| Mac OS X 10.9 (Mavericks) | ddgraph_1.10.1.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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