Extension and Robustness of Transitivity Clustering for Protein–Protein Interaction Network Analysis

Abstract

Partitioning biological data objects into groups such that the objects within the groups share common traits is a longstanding challenge in computational biology.

Recently, we developed and established transitivity clustering, a partitioning approach based on weighted transitive graph projection that utilizes a single similarity threshold as density parameter. In previous publications, we concentrated on the graphical user interface and on concrete biomedical application protocols. Here, we contribute the following theoretical considerations: (1) We provide proofs that the average similarity between objects from the same cluster is above the user-given threshold and that the average similarity between objects from different clusters is below the threshold. (2) We extend transitivity clustering to an overlapping clustering tool by integrating two new approaches. (3) We demonstrate the power of transitivity clustering for protein-complex detection. We evaluate our approaches against others by utilizing gold-standard data that was previously used by Brohée et al. for reviewing existing bioinformatics clustering tools.

The extended version of this article is available online at http://transclust.mpi-inf.mpg.de .

Acknowledgments

The work of JB was supported by the Cluster of Excellence for Multimodal Computing (MMCI). TW received support from NIH grant R01 LM009722 and the Buck Trust. RR is grateful for financial support of the International Max Planck Research School (IMPRS).

Notes

¹Modified graphs are available at http://rsat.bigre.ulb.ac.be/rsat/data/published_data/brohee_2006_clustering_evaluation/.