Skip to content

molsysbio/STASNet

Folders and files

NameName
Last commit message
Last commit date

Latest commit

d0052b9 · Mar 14, 2019
Mar 14, 2019
Mar 14, 2019
Jan 23, 2019
Mar 14, 2019
Mar 14, 2019
Mar 14, 2019
Aug 5, 2014
Dec 12, 2018
Feb 25, 2014
Feb 20, 2014
Feb 20, 2014
Mar 14, 2019
Aug 4, 2016
Jan 23, 2019
Feb 20, 2014
Jan 31, 2019
Nov 19, 2015
Nov 1, 2015
Nov 18, 2015

Repository files navigation

Copyright (c) Nils Blüthgen and Bertram Klinger and Mathurin Dorel, 2013-

Gnu General Public Licence version 3 (GPLv3)

---

The library contains the levmar-package (version 2.5) for optimization (c) by Manolis Lourakis
(see src/levmar-2.5/README.txt)

---

This library can be used to model signalling perturbation data using models that are derived from
Modular Response Analysis using R. 

Requires the following C++ libraries to be installed (installation method depends on the OS): 

    ginac
    cln
    pkg-config (at least Mac OS)

And the following R packages: 

    Rcpp (>= 0.10.4)
    BH
    RhpcBLASctl
    Rgraphviz
    pheatmap
    lattice
    lhs
    parallel

You can install those packages in R with:

    source("https://bioconductor.org/biocLite.R")
    biocLite(c("Rcpp","BH","RhpcBLASctl","Rgraphviz","pheatmap","lattice","lhs","parallel"))

Unix (MacOS, BSD, GNU/Linux) installation guide:
    For a local installation, first create a folder for local R library in (e.g "home/<Username>/R")
    Open shell go to the folder of the package (“STASNet”)
    execute: R CMD INSTALL ./ (when recompiling use R CMD INSTALL --preclean ./)
    
    Note: for some newer OS versions you have to specify the c++ version:
    prior to installing the package execute:
    export PKG_CXXFLAGS='`Rscript -e "Rcpp:::CxxFlags()"` -std=c++11'

Have fun with it! 

---

If you use this program in publications, please cite one of the following paper:

Dorel, M.; Klinger, B.; Sieber, A.; Prahallad, A.; Gross, T.; Bosdriesz, E.; Wessels, L. and Blüthgen, N.
Modelling Signalling Networks from Perturbation Data.
Bioinformatics, early online, 2018. [doi](https://doi.org/10.1093/bioinformatics/bty473) 
 
Klinger, B.; Sieber, A.; Fritsche-Guenther, R.; Witzel, F.; Berry, L.; Schumacher, D.; Yan, Y.; Durek, P.; Merchant, M.; Schäfer, R.; Sers, C. and Blüthgen, N.
Network quantification of EGFR signaling unveils potential for targeted combination therapy.
Molecular Systems Biology, 9: 673, 2013. [doi](http://dx.doi.org/10.1038/msb.2013.29)

About

No description, website, or topics provided.

Resources

License

Stars

Watchers

Forks

Packages

No packages published

Contributors 4