The Steiner tree problem on unweighted graphs seeks a minimum subtree (i.e. subtree with minimal number of edges), containing a given subset of the vertices (terminals). This problem is known to be NP-hard. This package provides several heuristic and one exact approach for finding Steiner trees, as well as tools for analyzing resultant trees and comparing different algorithms.

This R package was originally applied to analyzing biological networks. For more information about algorithms and their application, see original article and tutorial.

The installation process may differ to different versions of package. See information below.

To get the latest version of the package and install it from CRAN run the following command:

install.packages("SteinerNet")

To install older versions of packages from CRAN, you need install_version() from devtools package. SteinerNet v2.0 works with igraph, limma and RBGL packages.

install.packages("igraph")

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

source("https://bioconductor.org/biocLite.R")
biocLite("limma")

install_version("SteinerNet", version = "2.0")

The installation process of older versions is similar to previous, but note, that they works with igraph0 package.

install_version("igraph0", version = "0.5.7")

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

source("https://bioconductor.org/biocLite.R")
biocLite("limma")

install_version("SteinerNet", version = "1.x")

https://cran.r-project.org/src/contrib/Archive/SteinerNet/

To use this code in your research please cite this article related to this package:

Afshin Sadeghi and Holger Froehlich, "Steiner tree methods for optimal sub-network identification: an empirical study", BMC Bioinformatics 2013 14:144 doi:10.1186/1471-2105-14-144.