Comparison and evaluation of network clustering algorithms applied to genetic interaction networks

Lin Hou, Lin Wang, Arthur Berg, Minping Qian, Yunping Zhu, Fangting Li, Minghua Deng

Research output: Contribution to journalArticle

1 Scopus citations

Abstract

The goal of network clustering algorithms detect dense clusters in a network, and provide a first step towards the understanding of large scale biological networks. With numerous recent advances in biotechnologies, large-scale genetic interactions are widely available, but there is a limited understanding of which clustering algorithms may be most effective. In order to address this problem, we conducted a systematic study to compare and evaluate six clustering algorithms in analyzing genetic interaction networks, and investigated influencing factors in choosing algorithms. The algorithms considered in this comparison include hierarchical clustering, topological overlap matrix, bi-clustering, Markov clustering, Bayesian discriminant analysis based community detection, and variational Bayes approach to modularity. Both experimentally identified and synthetically constructed networks were used in this comparison. The accuracy of the algorithms is measured by the Jaccard index in comparing predicted gene modules with benchmark gene sets. The results suggest that the choice differs according to the network topology and evaluation criteria. Hierarchical clustering showed to be best at predicting protein complexes, Bayesian discriminant analysis based community detection proved best under epistatic miniarray profile (EMAP) datasets, the variational Bayes approach to modularity was noticeably better than the other algorithms in the genome-scale networks.

Original languageEnglish (US)
Pages (from-to)2150-2161
Number of pages12
JournalFrontiers in Bioscience - Elite
Volume4 E
Issue number6
StatePublished - Jan 1 2012

All Science Journal Classification (ASJC) codes

  • Biochemistry, Genetics and Molecular Biology(all)
  • Immunology and Microbiology(all)

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