On the statistical significance of protein complex

Abstract

Background: Statistical validation of predicted complexes is a fundamental issue in proteomics and bioinformatics. The target is to measure the statistical significance of each predicted complex in terms of p-values. Surprisingly, this issue has not received much attention in the literature. To our knowledge, only a few research efforts have been made towards this direction.

Methods: In this article, we propose a novel method for calculating the p-value of a predicted complex. The null hypothesis is that there is no difference between the number of edges in target protein complex and that in the random null model. In addition, we assume that a true protein complex must be a connected subgraph. Based on this null hypothesis, we present an algorithm to compute the p-value of a given predicted complex.

Results: We test our method on five benchmark data sets to evaluate its effectiveness.

Conclusions: The experimental results show that our method is superior to the state-of-the-art algorithms on assessing the statistical significance of candidate protein complexes.

Graphical abstract

Keywords

predicted complex / statistical significance testing / subgraph mining / community detection

References

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AUTHOR CONTRIBUITIONS

Youfu Su drafted the manuscript. Zheng Chen and Bo Tian performed the implementations. Can Zhao and Zengyou He conceived the study and finalized the manuscript. All authors read and approved the final manuscript.

ACKNOWLEDGEMENTS

This work was partially supported by the National Natural Science Foundation of China under (No. 61572094), the Fundamental Research Funds for the Central Universities of China (Nos. DUT2017TB02 and DUT14QY07). Additionally, we want to thank the academic support received from Mr. Ben Teng and Dr. Xiuli Ma.

COMPLIANCE WITH ETHICS GUIDELINES

The authors Youfu Su, Can Zhao, Zheng Chen, Bo Tian and Zengyou He declare that they have no conflict of interests.

This article does not contain any studies with human or animal subjects performed by any of the authors.

RIGHTS & PERMISSIONS

2018 Higher Education Press and Springer-Verlag GmbH Germany, part of Springer Nature