SegCorr a statistical procedure for the detection of genomic regions of correlated expression

BMC Bioinformatics

Methodology Article

Emilie Lebarbier¹  Stéphane Robin¹  Tristan Mary-Huard²  Eleni Ioanna Delatola³  François Radvanyi⁴  Jennifer Wong⁵ 

[1]AgroParisTech UMR518, 75005, Paris, France

[2]INRA UMR518, 75005, Paris, France

[3]AgroParisTech UMR518, 75005, Paris, France

[4]INRA UMR518, 75005, Paris, France

[5]INRA, UMR 0320 - UMR 8120 Genetique Quantitative et Evolution-Le Moulon, F-91190, Gif-sur-Yvette, France

[6]AgroParisTech UMR518, 75005, Paris, France

[7]INRA UMR518, 75005, Paris, France

[8]Institut Curie, PSL Research University, Cedex 05, 75248, Paris, France

[9]CNRS UMR144, Equipe Labellisee par La Ligue Nationale contre le Cancer, Cedex 05, 75248, Paris, France

[10]Institut Curie, PSL Research University, Cedex 05, 75248, Paris, France

[11]CNRS UMR144, Equipe Labellisee par La Ligue Nationale contre le Cancer, Cedex 05, 75248, Paris, France

[12]Institut Curie, PSL Research University, Cedex 05, 75248, Paris, France

[13]CNRS UMR144, Equipe Labellisee par La Ligue Nationale contre le Cancer, Cedex 05, 75248, Paris, France

[14]Molecular Oncology Unit, Department of Biochemistry, Hospital Saint Louis, AP-HP, Cedex 10, 75475, Paris, France

[15]Université Paris Diderot, Sorbonne Paris Cité, CNRS UMR7212/INSERM U944, Cedex 10, 75475, Paris, France

关键词: Gene expression;    Chromosomes;    Correlation matrix segmentation;    CNV;    DNA Methylation;    SegCorr;   

DOI  :  10.1186/s12859-017-1742-5

 received in 2016-06-07, accepted in 2017-06-26,  发布年份 2017

来源: Springer

【 摘 要 】

BackgroundDetecting local correlations in expression between neighboring genes along the genome has proved to be an effective strategy to identify possible causes of transcriptional deregulation in cancer. It has been successfully used to illustrate the role of mechanisms such as copy number variation (CNV) or epigenetic alterations as factors that may significantly alter expression in large chromosomal regions (gene silencing or gene activation).ResultsThe identification of correlated regions requires segmenting the gene expression correlation matrix into regions of homogeneously correlated genes and assessing whether the observed local correlation is significantly higher than the background chromosomal correlation. A unified statistical framework is proposed to achieve these two tasks, where optimal segmentation is efficiently performed using dynamic programming algorithm, and detection of highly correlated regions is then achieved using an exact test procedure. We also propose a simple and efficient procedure to correct the expression signal for mechanisms already known to impact expression correlation. The performance and robustness of the proposed procedure, called SegCorr, are evaluated on simulated data. The procedure is illustrated on cancer data, where the signal is corrected for correlations caused by copy number variation. It permitted the detection of regions with high correlations linked to epigenetic marks like DNA methylation.ConclusionsSegCorr is a novel method that performs correlation matrix segmentation and applies a test procedure in order to detect highly correlated regions in gene expression.

【 授权许可】

CC BY   
© The Author(s) 2017

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