Bioinformatics Advance Access published online on March 14, 2008
Bioinformatics, doi:10.1093/bioinformatics/btn079
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Fitting a Geometric Graph to a Protein-Protein Interaction Network
ajski 2,3a Pr
ulj 2
1Department of Mathematics, University of Strathclyde, Glasgow G1 1XH, UK
2Department of Computer Science, UC Irvine, Irvine, CA, 92697, USA
3Faculty of Electrical Engineering, University of Belgrade, Belgrade, Serbia
To whom correspondence should be addressed. Nataa Prulj, E-mail: natasha{at}ics.uci.edu
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Abstract |
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Motivation: Finding a good network null model for protein-protein interaction (PPI) networks is a fundamental issue. Such a model would provide insights into the interplay between network structure and biological function as well as into evolution. Also, network (graph) models are used to guide biological experiments and discover new biological features. It has been proposed that geometric random graphs are a good model for PPI networks. In a geometric random graph, nodes correspond to uniformly randomly distributed points in a metric space and edges (links) exist between pairs of nodes for which the corresponding points in the metric space are close enough according to some distance norm. Computational experiments have revealed close matches between key topological properties of PPI networks and geometric random graph models. In this work, we push the comparison further by exploiting the fact that the geometric property can be tested for directly. To this end, we develop an algorithm that takes PPI interaction data and embeds proteins into a low dimensional Euclidean space, under the premise that connectivity information corresponds to Euclidean proximity, as in geometric random graphs. We judge the sensitivity and specificity of the fit by computing the area under the Receiver Operator Characteristic (ROC) curve. The network embedding algorithm is based on Multi-Dimensional Scaling, with the square root of the path length in a network playing the role of the Euclidean distance in the Euclidean space. The algorithm exploits parsity for computational efficiency, and requires only a few sparse matrix multiplications, giving a complexity of O(N2) where N is the number of proteins.
Results: The algorithm has been verified in the sense that it successfully rediscovers the geometric structure in artificially constructed geometric networks, even when noise is added by re-wiring some links. Applying the algorithm to 19 publicly available PPI networks of various organisms indicated that: (a) geometric effects are present, and (b) two-dimensional Euclidean space is generally as effective as higher-dimensional Euclidean space for explaining the connectivity. Testing on a high-confidence yeast data set produced a very strong indication of geometric structure (area under the ROC curve of 0.89), with this network being essentially indistinguishable from a noisy geometric network. Overall, the results add support to the hypothesis that PPI networks have a geometric structure.
Availability: MATLAB code implementing the algorithm is available upon request.
Contact: natasha{at}ics.uci.edu
Associate Editor: Dr Olga Troyanskaya
Received on September 19, 2007; revised on February 8, 2008; accepted on February 27, 2008
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