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Resolving the structure of interactomes with hierarchical agglomerative clustering.

Park Y, Bader JS - BMC Bioinformatics (2011)

Bottom Line: 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.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.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21218, USA. ypark28@jhu.edu

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.

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Interaction enrichment within clusters. Black solid lines: Edge-density distribution of the top-level clusters; Red dashed lines: Edge-density distribution of the bottom-level clusters. A, B, C: Each panel respectively corresponds to the result of YEAST-PPI, YEAST-GEN, and YEAST-SGA datasets.
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Figure 3: Interaction enrichment within clusters. Black solid lines: Edge-density distribution of the top-level clusters; Red dashed lines: Edge-density distribution of the bottom-level clusters. A, B, C: Each panel respectively corresponds to the result of YEAST-PPI, YEAST-GEN, and YEAST-SGA datasets.

Mentions: Edge densities within top-level clusters and bottom-level clusters have bimodal distributions, including edge densities of both 0 and 1 (Fig. 3). Clusters with density 0 can be generated when unconnected vertices share one or more interaction partners, a frequent pattern in both physical and genetic interaction networks. Standard algorithms for identifying densely connected subnetworks [1,2,28] perform poorly in these cases, whereas algorithms based on shared neighbors can still perform well [29].


Resolving the structure of interactomes with hierarchical agglomerative clustering.

Park Y, Bader JS - BMC Bioinformatics (2011)

Interaction enrichment within clusters. Black solid lines: Edge-density distribution of the top-level clusters; Red dashed lines: Edge-density distribution of the bottom-level clusters. A, B, C: Each panel respectively corresponds to the result of YEAST-PPI, YEAST-GEN, and YEAST-SGA datasets.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3044301&req=5

Figure 3: Interaction enrichment within clusters. Black solid lines: Edge-density distribution of the top-level clusters; Red dashed lines: Edge-density distribution of the bottom-level clusters. A, B, C: Each panel respectively corresponds to the result of YEAST-PPI, YEAST-GEN, and YEAST-SGA datasets.
Mentions: Edge densities within top-level clusters and bottom-level clusters have bimodal distributions, including edge densities of both 0 and 1 (Fig. 3). Clusters with density 0 can be generated when unconnected vertices share one or more interaction partners, a frequent pattern in both physical and genetic interaction networks. Standard algorithms for identifying densely connected subnetworks [1,2,28] perform poorly in these cases, whereas algorithms based on shared neighbors can still perform well [29].

Bottom Line: 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.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.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21218, USA. ypark28@jhu.edu

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.

Show MeSH