Differential community detection in paired biological networks

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

Motivation: Biological networks unravel the inherent structure of molecular interactions which can lead to discovery of driver genes and meaningful pathways especially in cancer context. Often due to gene mutations, the gene expression undergoes changes and the corresponding gene regulatory network sustains some amount of localized re-wiring. The ability to identify significant changes in the interaction patterns caused by the progression of the disease can lead to the revelation of novel relevant signatures. Methods: The task of identifying differential sub-networks in paired biological networks (A:control,B:case) can be re-phrased as one of finding dense communities in a single noisy differential topological (DT) graph constructed by taking absolute difference between the topological graphs of A and B. In this paper, we propose a fast three-stage approach, namely Differential Community Detection (DCD), to identify differential sub-networks as differential communities in a de-noised version of the DT graph. In the first stage, we iteratively re-order the nodes of the DT graph to determine approximate block diagonals present in the DT adjacency matrix using neighbourhood information of the nodes and Jaccard similarity. In the second stage, the ordered DT adjacency matrix is traversed along the diagonal to remove all the edges associated with a node, if that node has no immediate edges within a window. Finally, we apply community detection methods on this de-noised DT graph to discover differential sub-networks as communities. Results: Our proposed DCD approach can effectively locate differential sub-networks in several simulated paired random-geometric networks and various paired scale-free graphs with different powerlaw exponents. The DCD approach easily outperforms community detection methods applied on the original noisy DT graph and recent statistical techniques in simulation studies. We applied DCD method on two real datasets: a) Ovarian cancer dataset to discover differential DNA co-methylation sub-networks in patients and controls; b) Glioma cancer dataset to discover the difference between the regulatory networks of IDH-mutant and IDH-wild-type. We demonstrate the potential benefits of DCD for finding networkinferred bio-markers/pathways associated with a trait of interest. Conclusion: The proposed DCD approach overcomes the limitations of previous statistical techniques and the issues associated with identifying differential sub-networks by use of community detection methods on the noisy DT graph. This is reflected in the superior performance of the DCD method with respect to various metrics like Precision, Accuracy, Kappa and Specificity. The code implementing proposed DCD method is available at https://sites.google.com/site/raghvendramallmlresearcher/codes.

Original languageEnglish
Title of host publicationACM-BCB 2017 - Proceedings of the 8th ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics
PublisherAssociation for Computing Machinery, Inc
Pages330-339
Number of pages10
ISBN (Electronic)9781450347228
DOIs
Publication statusPublished - 20 Aug 2017
Event8th ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics, ACM-BCB 2017 - Boston, United States
Duration: 20 Aug 201723 Aug 2017

Other

Other8th ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics, ACM-BCB 2017
CountryUnited States
CityBoston
Period20/8/1723/8/17

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Keywords

  • Community Detection
  • Differential sub-networks
  • Network comparisons
  • Systems biology

ASJC Scopus subject areas

  • Software
  • Biomedical Engineering
  • Health Informatics
  • Computer Science Applications

Cite this

Mall, R., D'Angelo, F., Ullah, E., Bensmail, H., Kunji, K., & Ceccarelli, M. (2017). Differential community detection in paired biological networks. In ACM-BCB 2017 - Proceedings of the 8th ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics (pp. 330-339). Association for Computing Machinery, Inc. https://doi.org/10.1145/3107411.3107418