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To install this package, start R and enter:
source("http://bioconductor.org/biocLite.R")
biocLite("CellNOptR")
In most cases, you don't need to download the package archive at all.
CellNOptR
Training of boolean logic models of signalling networks using prior knowledge networks and perturbation data.
Bioconductor version: 3.0
This package does optimisation of boolean logic networks of signalling pathways based on a previous knowledge network and a set of data upon perturbation of the nodes in the network.
Author: T.Cokelaer, F.Eduati, A.MacNamara, S.Schrier, C.Terfve
Maintainer: T.Cokelaer <cokelaer at ebi.ac.uk>
Citation (from within R,
enter citation("CellNOptR")):
Installation
To install this package, start R and enter:
source("http://bioconductor.org/biocLite.R")
biocLite("CellNOptR")
Documentation
To view documentation for the version of this package installed in your system, start R and enter:
browseVignettes("CellNOptR")
| R Script | Main vignette:Playing with networks using CellNOptR | |
| Reference Manual | ||
| Text | NEWS |
Details
| biocViews | CellBasedAssays, CellBiology, Proteomics, Software, TimeCourse |
| Version | 1.12.0 |
| In Bioconductor since | BioC 2.9 (R-2.14) |
| License | GPL-3 |
| Depends | R (>= 2.15.0), RBGL, graph, methods, hash, ggplot2, RCurl, Rgraphviz, XML |
| Imports | |
| LinkingTo | |
| Suggests | RUnit, BiocGenerics, igraph |
| SystemRequirements | Graphviz version >= 2.2 |
| Enhances | |
| URL | |
| Depends On Me | CNORdt, CNORfeeder, CNORfuzzy, CNORode |
| Imports Me | |
| Suggests Me | MEIGOR |
| Build Report |
Package Archives
Follow Installation instructions to use this package in your R session.
| Package Source | CellNOptR_1.12.0.tar.gz |
| Windows Binary | CellNOptR_1.12.0.zip (32- & 64-bit) |
| Mac OS X 10.6 (Snow Leopard) | CellNOptR_1.12.0.tgz |
| Mac OS X 10.9 (Mavericks) | CellNOptR_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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