PCSF: An R-package for network-based interpretation of high-throughput data
Name
journal.pcbi.1005694.pdf
Size
1.48 MB
Format
Adobe PDF
Checksum (MD5)
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Author(s)
Montemanni, Roberto
Bertoni, Francesco
Kwee, Ivo
Akhmedov, Murodzhon
Kedaigle, Amanda Joy
Escalante, Renan A.
Fraenkel, Ernest
Date Issued
July 2017
Journal
PLOS Computational Biology
Publisher
Public Library of Science (PLoS)
Citation
Akhmedov, Murodzhon, et al. “PCSF: An R-Package for Network-Based Interpretation of High-Throughput Data.” PLOS Computational Biology, edited by Dina Schneidman, vol. 13, no. 7, July 2017, p. e1005694.
Abstract
With the recent technological developments a vast amount of high-throughput data has been profiled to understand the mechanism of complex diseases. The current bioinformatics challenge is to interpret the data and underlying biology, where efficient algorithms for analyzing heterogeneous high-throughput data using biological networks are becoming increasingly valuable. In this paper, we propose a software package based on the Prize-collecting Steiner Forest graph optimization approach. The PCSF package performs fast and user-friendly network analysis of high-throughput data by mapping the data onto a biological networks such as protein-protein interaction, gene-gene interaction or any other correlation or coexpression based networks. Using the 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.
MIT Department
Massachusetts Institute of Technology. Department of Biological Engineering
Terms of Use
Creative Commons Attribution 4.0 International License
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DOI of Published Version
https://doi.org/10.1371/JOURNAL.PCBI.1005694
