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Open Access Highly Accessed Methodology article

Link-based quantitative methods to identify differentially coexpressed genes and gene Pairs

Hui Yu123, Bao-Hong Liu34, Zhi-Qiang Ye13, Chun Li56, Yi-Xue Li134* and Yuan-Yuan Li13*

Author Affiliations

1 Bioinformatics Center, Key Laboratory of Systems Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, 320 Yueyang Road, Shanghai 200031, P. R. China

2 Graduate University of the Chinese Academy of Sciences, 19A Yuquanlu, Beijing 100049, P. R. China

3 Shanghai Center for Bioinformation Technology, 100 Qinzhou Road, Shanghai 200235, P. R. China

4 School of Life Science and Technology, Tongji University, 1239 Siping Road, Shanghai 200092, P.R. China

5 Department of Biostatistics, Vanderbilt University School of Medicine, Nashville, TN 37232, USA

6 Center for Human Genetics Research, Vanderbilt University School of Medicine, Nashville, TN 37232, USA

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BMC Bioinformatics 2011, 12:315  doi:10.1186/1471-2105-12-315

Published: 2 August 2011

Abstract

Background

Differential coexpression analysis (DCEA) is increasingly used for investigating the global transcriptional mechanisms underlying phenotypic changes. Current DCEA methods mostly adopt a gene connectivity-based strategy to estimate differential coexpression, which is characterized by comparing the numbers of gene neighbors in different coexpression networks. Although it simplifies the calculation, this strategy mixes up the identities of different coexpression neighbors of a gene, and fails to differentiate significant differential coexpression changes from those trivial ones. Especially, the correlation-reversal is easily missed although it probably indicates remarkable biological significance.

Results

We developed two link-based quantitative methods, DCp and DCe, to identify differentially coexpressed genes and gene pairs (links). Bearing the uniqueness of exploiting the quantitative coexpression change of each gene pair in the coexpression networks, both methods proved to be superior to currently popular methods in simulation studies. Re-mining of a publicly available type 2 diabetes (T2D) expression dataset from the perspective of differential coexpression analysis led to additional discoveries than those from differential expression analysis.

Conclusions

This work pointed out the critical weakness of current popular DCEA methods, and proposed two link-based DCEA algorithms that will make contribution to the development of DCEA and help extend it to a broader spectrum.