MRHCA: a nonparametric statistics based method for hub and co-expression module identification in large gene co-expression network

Abstract

Background: Gene co-expression and differential co-expression analysis has been increasingly used to study co-functional and co-regulatory biological mechanisms from large scale transcriptomics data sets.

Methods: In this study, we develop a nonparametric approach to identify hub genes and modules in a large co-expression network with low computational and memory cost, namely MRHCA.

Results: We have applied the method to simulated transcriptomics data sets and demonstrated MRHCA can accurately identify hub genes and estimate size of co-expression modules. With applying MRHCA and differential co-expression analysis to E. coli and TCGA cancer data, we have identified significant condition specific activated genes in E. coli and distinct gene expression regulatory mechanisms between the cancer types with high copy number variation and small somatic mutations.

Conclusion: Our analysis has demonstrated MRHCA can (i) deal with large association networks, (ii) rigorously assess statistical significance for hubs and module sizes, (iii) identify co-expression modules with low associations, (iv) detect small and significant modules, and (v) allow genes to be present in more than one modules, compared with existing methods.

Graphical abstract

Keywords

gene co-expression network / algorithm for large scale networks analysis / statistical significance of gene co-expression / Mutual Rank

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SUPPLEMENTARY MATERIALS

The supplementary materials can be found online with this article at 10.1007/s40484-018-0131-z.

ACKNOWLEDGEMENTS

This work was supported by the Showalter Young Investigator Award, Indiana CTSI; by National Science Foundation/EPSCoR Award (No. IIA-1355423), the State of South Dakota Research Innovation Center, the Agriculture Experiment Station of South Dakota State University, and the National Science Foundation of the United States (No.1546869). This research used the Extreme Science and Engineering Discovery Environment (XSEDE), which is supported by National Science Foundation grant ( No. ACI-1548562).

COMPLIANCE WITH ETHICS GUIDELINES

The authors Yu Zhang, Sha Cao, Jing Zhao, Burair Alsaihati, Qin Ma and Chi Zhang 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.

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