title: Resolving the structure of interactomes with hierarchical agglomerative clustering.
abstract: BACKGROUND
  Graphs provide a natural framework for visualizing and analyzing networks of many types, including biological networks. Network clustering is a valuable approach for summarizing the structure in large networks, for predicting unobserved interactions, and for predicting functional annotations. Many current clustering algorithms suffer from a common set of limitations: poor resolution of top-level clusters; over-splitting of bottom-level clusters; requirements to pre-define the number of clusters prior to analysis; and an inability to jointly cluster over multiple interaction types.
  
  
  RESULTS
  A new algorithm, Hierarchical Agglomerative Clustering (HAC), is developed for fast clustering of heterogeneous interaction networks. This algorithm uses maximum likelihood to drive the inference of a hierarchical stochastic block model for network structure. Bayesian model selection provides a principled method for collapsing the fine-structure within the smallest groups, and for identifying the top-level groups within a network. Model scores are additive over independent interaction types, providing a direct route for simultaneous analysis of multiple interaction types. In addition to inferring network structure, this algorithm generates link predictions that with cross-validation provide a quantitative assessment of performance for real-world examples.
  
  
  CONCLUSIONS
  When applied to genome-scale data sets representing several organisms and interaction types, HAC provides the overall best performance in link prediction when compared with other clustering methods and with model-free graph diffusion kernels. Investigation of performance on genome-scale yeast protein interactions reveals roughly 100 top-level clusters, with a long-tailed distribution of cluster sizes. These are in turn partitioned into 1000 fine-level clusters containing 5 proteins on average, again with a long-tailed size distribution. Top-level clusters correspond to broad biological processes, whereas fine-level clusters correspond to discrete complexes. Surprisingly, link prediction based on joint clustering of physical and genetic interactions performs worse than predictions based on individual data sets, suggesting a lack of synergy in current high-throughput data.
authors.affiliation: Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21218, USA. ypark28@jhu.edu
authors.forename: Yongjin
authors.initials: Y
authors.lastname: Park
authors.forename: Joel S
authors.initials: JS
authors.lastname: Bader
mesh_terms: D000465:Algorithms; D001499:Bayes Theorem; D016000:Cluster Analysis; D019295:Computational Biology / Q000379:methods*; D053263:Gene Regulatory Networks; D016013:Likelihood Functions; D015233:Models, Statistical; D025941:Protein Interaction Mapping / Q000379:methods*; D012441:Saccharomyces cerevisiae / Q000235:genetics; D012984:Software