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Bioinformatics Advance Access published online on September 25, 2008
Bioinformatics, doi:10.1093/bioinformatics/btn499
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© The Author (2008). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org

Identifying differentially-expressed subnetworks with MMG

Josselin Noirel 1,*, Guido Sanguinetti 2 and Phillip C. Wright 1

1Biological and Environmental Systems Group, Department of Chemical and Process Engineering, University 10 of Sheffield, Mappin Street, Sheffield, S1 3JD, UK.
2Department of Computer Science, University of Sheffield, Regent Court, 211 Portobello Road, Sheffield, S1 4DP, UK.

*To whom correspondence should be addressed. Dr. Josselin Noirel, E-mail: j.noirel{at}sheffield.ac.uk


  Abstract

Background Mixture Model on Graphs (MMG) is a probabilistic model that integrates network topology with (gene, protein) expression data to predict the regulation state of genes and proteins from high-throughput data. It is remarkably robust to missing data, a feature particularly important for its use in Quantitative Proteomics. A new implementation in C and interfaced with R makes MMG extremely fast and easy to use and to extend.

Availability The original implementation (Matlab) is still available from http://www.dcs.shef.ac.uk/~guido/; the new implementation is available from http://wrightlab.group.shef.ac.uk/people_noirel.htm, from CRAN, and has been submitted to BioConductor, http://www.bioconductor.org/.

Contact j.noirel{at}sheffield.ac.uk

Associate Editor: Dr. Trey Ideker

Received on May 26, 2008; revised on May 26, 2008; accepted on September 19, 2008

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