PCSF: an R-Package for Network-Based Interpretation of High-throughput Data

The PCSF package performs fast and user-friendly network analysis of high-throughput data. Using interaction networks as a template, it determines high-confidence subnetworks relevant to the data, which potentially leads to predictions of functional units. It also interactively visualizes the resulting subnetwork with functional enrichment analysis.

Contact: Murodzhon Akhmedov [[email protected]]

Reference:

A divide and conquer matheuristic algorithm for the Prize-collecting Steiner Tree Problem. Akhmedov M, Kwee I, and Montemanni R (2016). Computers and Operations Research, 70, 18-25.

A fast Prize-collecting Steiner Forest algorithm for Functional Analyses in Biological Networks. Akhmedov M, LeNail A, Bertoni F, Kwee I, Fraenkel E and Montemanni R (2017). Lecture Notes in Computer Science, to appear.

System Requirements:

1. R (>= 3.1.0)

2. Boost C++ library: http://www.boost.org

Installation:

1. The PCSF package depends on the following R-packages:

• BH and igraph - for efficient graph handling and calculations,
• httr, methods, org.Hs.eg.db, and topGO - to perform enrichment analysis,
• Rcpp - to employ C++ source code within R,
• visNetwork - for visualization.

2. In order to compile the source, Windows users should install the Rtools package by the following link that installs GCC and CMake.

3. The PCSF package and its dependencies can be installed on Mac OS, Linux and Windows by running the following commands in the R console.

source("http://bioconductor.org/biocLite.R")
biocLite("topGO")
install.packages("devtools", dependencies=TRUE)
devtools::install_github("IOR-Bioinformatics/PCSF", repos=BiocInstaller::biocinstallRepos(),
                         dependencies=TRUE, type="source", force=TRUE)

Comments:

Test environments

• Mac OS X (10.12.4), R 3.4.0
• Ubuntu (16.04), R 3.2.3
• Windows 7, R 3.4.1

R CMD check results

There were no ERRORs, WARNINGs or NOTEs.