A matrix rank based concordance index for evaluating and detecting conditional specific co-expressed gene modules

Zhi Han, Jie Zhang, Guoyuan Sun, Gang Liu, Kun Huang

Research output: Contribution to journalArticle

7 Scopus citations

Abstract

Background: Gene co-expression network analysis (GCNA) is widely adopted in bioinformatics and biomedical research with applications such as gene function prediction, protein-protein interaction inference, disease markers identification, and copy number variance discovery. Currently there is a lack of rigorous analysis on the mathematical condition for which the co-expressed gene module should satisfy. Methods: In this paper, we present a linear algebraic based Centralized Concordance Index (CCI) for evaluating the concordance of co-expressed gene modules from gene co-expression network analysis. The CCI can be used to evaluate the performance for co-expression network analysis algorithms as well as for detecting condition specific co-expression modules. We applied CCI in detecting lung tumor specific gene modules. Results and Discussion: Simulation showed that CCI is a robust indicator for evaluating the concordance of a group of co-expressed genes. The application to lung cancer datasets revealed interesting potential tumor specific genetic alterations including CNVs and even hints for gene-fusion. Deeper analysis required for understanding the molecular mechanisms of all such condition specific co-expression relationships. Conclusion: The CCI can be used to evaluate the performance for co-expression network analysis algorithms as well as for detecting condition specific co-expression modules. It is shown to be more robust to outliers and interfering modules than density based on Pearson correlation coefficients.

Original languageEnglish (US)
Article number519
JournalBMC genomics
Volume17
DOIs
StatePublished - Aug 22 2016

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ASJC Scopus subject areas

  • Biotechnology
  • Genetics

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