PCSF: An R-package for network-based interpretation of high-throughput data

Murodzhon Akhmedov, Amanda Kedaigle, Renan Escalante Chong, Roberto Montemanni, Francesco Bertoni, Ernest Fraenkel and Ivo Kwee

PLOS Computational Biology, 2017, vol. 13, issue 7, 1-7

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.

Date: 2017
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pcbi00:1005694

DOI: 10.1371/journal.pcbi.1005694

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