/CellNOptR

Training of boolean logic models of signalling networks using prior knowledge networks and perturbation data.
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  2. C 10.3%
  3. Python 2.0%
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NAMESPACE
Readme.md installation instructions added to readme Apr 18, 2018

Readme.md

CellNOptR

Training of boolean logic models of signalling networks using prior knowledge networks and perturbation data.

  • Please visit CellNOptR for details about the project (references, news, ...)

Installation:

Before starting, make sure you have installed the latest version of R. For more information and download of R, please refer to R project page <http://www.r-project.org/>_ . For more information about how to install R packages, please refer to Installing package <http://cran.r-project.org/doc/manuals/R-admin.html#Installing-packages>_ These packages rely on several Bioconductor package (e.g., RBGL, graph, methods, etc.). As an example, you can install RGBL package by typing:

  source("http://bioconductor.org/biocLite.R")
  biocLite("RBGL")

Installation from GitHub

using the devtools package you can install the latest version from the GitHub repository:

if(!require("devtools")) install.packages('devtools’)   # installs devtools package if not already installed
devtools::install_github('saezlab/CellNOptR’)

Standard installation from Bioconductor

To install CellNOptR, type:

  source("http://bioconductor.org/biocLite.R")
  biocLite("CellNOptR")

Then, you can also install other CellNOptR related packages::

   biocLite("CNORdt")
   biocLite("CNORfeeder")
   biocLite("CNORfuzzy")
   biocLite("CNORode")

Install from a local copy of the package:

install CellNOptR from a tar ball as follows:

install.packages("path_to_CellNOptR/CellNOptR_1.0.0.tar.gz", + repos=NULL, type="source")

or, using the R GUI by clicking on "Packages & Data" then "Package installer", then choosing "local source" from the dropdown menu, clicking "install", choosing CellNOptR.1.0.0.tar.gz and finally clicking "open".