Detection of statistically significant network changes in complex biological networks

RaghvenPhDa Mall, Luigi Cerulo, Halima Bensmail, Antonio Iavarone, Michele Ceccarelli

Research output: Contribution to journalArticle

8 Citations (Scopus)

Abstract

Background: Biological networks contribute effectively to unveil the complex structure of molecular interactions and to discover driver genes especially in cancer context. It can happen that due to gene mutations, as for example when cancer progresses, the gene expression network undergoes some amount of localized re-wiring. The ability to detect statistical relevant changes in the interaction patterns induced by the progression of the disease can lead to the discovery of novel relevant signatures. Several procedures have been recently proposed to detect sub-network differences in pairwise labeled weighted networks. Methods: In this paper, we propose an improvement over the state-of-the-art based on the Generalized Hamming Distance adopted for evaluating the topological difference between two networks and estimating its statistical significance. The proposed procedure exploits a more effective model selection criteria to generate p-values for statistical significance and is more efficient in terms of computational time and prediction accuracy than literature methods. Moreover, the structure of the proposed algorithm allows for a faster parallelized implementation. Results: In the case of dense random geometric networks the proposed approach is 10-15x faster and achieves 5-10% higher AUC, Precision/Recall, and Kappa value than the state-of-the-art. We also report the application of the method to dissect the difference between the regulatory networks of IDH-mutant versus IDH-wild-type glioma cancer. In such a case our method is able to identify some recently reported master regulators as well as novel important candidates. Conclusions: We show that our network differencing procedure can effectively and efficiently detect statistical significant network re-wirings in different conditions. When applied to detect the main differences between the networks of IDH-mutant and IDH-wild-type glioma tumors, it correctly selects sub-networks centered on important key regulators of these two different subtypes. In addition, its application highlights several novel candidates that cannot be detected by standard single network-based approaches.

Original languageEnglish
Article number32
JournalBMC Systems Biology
Volume11
Issue number1
DOIs
Publication statusPublished - 4 Mar 2017

Fingerprint

Biological Networks
Electric wiring
Complex Networks
Genes
Hamming distance
Molecular interactions
Gene expression
Tumors
Cancer
Glioma
Statistical Significance
Mutant
Regulator
Neoplasms
Gene Regulatory Networks
Neoplasm Genes
Dissect
Gene
Molecular Structure
Model Selection Criteria

Keywords

  • Differential networks
  • Gene regulatory network inference
  • Master regulators

ASJC Scopus subject areas

  • Structural Biology
  • Modelling and Simulation
  • Molecular Biology
  • Computer Science Applications
  • Applied Mathematics

Cite this

Detection of statistically significant network changes in complex biological networks. / Mall, RaghvenPhDa; Cerulo, Luigi; Bensmail, Halima; Iavarone, Antonio; Ceccarelli, Michele.

In: BMC Systems Biology, Vol. 11, No. 1, 32, 04.03.2017.

Research output: Contribution to journalArticle

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