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