Research article

Searching for functional gene modules with interaction component models

Juuso A Parkkinen^1,2 and Samuel Kaski¹

• Corresponding authors: Juuso A Parkkinen juuso.parkkinen@tkk.fi - Samuel Kaski samuel.kaski@tkk.fi

Author Affiliations

¹ Helsinki Institute for Information Technology HIIT and Adaptive Informatics Research Centre, Department of Information and Computer Science, Helsinki University of Technology, P.O. Box 5400, FI-02015 TKK, Finland

² Department of Computer Science, P.O. Box 68, FI-00014, University of Helsinki, Finland

BMC Systems Biology 2010, 4:4 doi:10.1186/1752-0509-4-4

Published: 25 January 2010

Abstract

Background

Functional gene modules and protein complexes are being sought from combinations of gene expression and protein-protein interaction data with various clustering-type methods. Central features missing from most of these methods are handling of uncertainty in both protein interaction and gene expression measurements, and in particular capability of modeling overlapping clusters. It would make sense to assume that proteins may play different roles in different functional modules, and the roles are evidenced in their interactions.

Results

We formulate a generative probabilistic model for protein-protein interaction links and introduce two ways for including gene expression data into the model. The model finds interaction components, which can be interpreted as overlapping clusters or functional modules. We demonstrate the performance on two data sets of yeast Saccharomyces cerevisiae. Our methods outperform a representative set of earlier models in the task of finding biologically relevant modules having enriched functional classes.

Conclusions

Combining protein interaction and gene expression data with a probabilistic generative model improves discovery of modules compared to approaches based on either data source alone. With a fairly simple model we can find biologically relevant modules better than with alternative methods, and in addition the modules may be inherently overlapping in the sense that different interactions may belong to different modules.