  Motivation: Detecting local correlations in expression between neighbor 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 chromosomic regions (gene silencing or gene activation).
  Results: The 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 possibly caused by copy number variation. The
correction permitted the detection of regions with high correlations linked to
DNA methylation.
  Availability and implementation: R package SegCorr is available on the CRAN.

Methodology