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

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CellNOptR

Lifecycle: stable BioC status

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 . For more information about how to install R packages, please refer to Installing packages.

Standard installation from Bioconductor

To install CellNOptR, type:

if (!requireNamespace("BiocManager", quietly = TRUE))
    install.packages("BiocManager")
BiocManager::install("CellNOptR", version = "3.8")

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

BiocManager::install("CNORdt")
BiocManager::install("CNORfeeder")
BiocManager::install("CNORfuzzy")
BiocManager::install("CNORode")

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", build_vignettes = TRUE)

To see the documentation of the package and a few examples, please type:

vignette("CellNOptR-vignette")

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".

Feedbacks, bug reports, features

Feedbacks and bugreports are always very welcomed!
Please use the Github issue page to report bugs or for questions: https://github.com/saezlab/CellNOptR/issues. Many thanks!

Code of Conduct

Please note that the CellNOptR project is released with a Contributor Code of Conduct. By contributing to this project, you agree to abide by its terms.

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

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