Detecting Correlated Genomic Regions

Description

Performs correlation matrix segmentation and applies a test procedure to detect highly correlated regions in gene expression. The segmentation procedure detects changes in the patterns of the gene expression correlation matrix. The test procedure asseses which regions exhibit a significantly high level of correlation. Additionally, a preprocessing procedure is provided to correct gene expression for copy number variation.

Details

Author(s)

E. I. Delatola, E. Lebarbier, T. Mary-Huard, F. Radvanyi, S. Robin, J. Wong.

Maintainer: Eleni Ioanna Delatola <eldelatola@yahoo.gr>

References

Delatola E. I., Lebarbier E., Mary-Huard T., Radvanyi F., Robin S., Wong J.(2015). SegCorr: a statistical procedure for the detection of genomic regions of correlated expression. Preprint on Arxiv.

See Also

multiseg

Examples

#data.sets = c('SNP','EXP_raw')
## Each gene corresponds to one SNP probe ## 
#Position_EXP = matrix(1:1000,nrow=500,byrow=TRUE)
#Position_SNP = seq(2,1000,by=2)
#data(list=data.sets)
#CHR = rep(1,dim(EXP_raw)[1])
#SNP.CHR = rep(1,dim(SNP)[1])

#results = SegCorr(CHR = CHR, EXP = EXP_raw, CNV = TRUE, SNPSMOOTH=TRUE,
#Position.EXP = Position_EXP, SNP.CHR = SNP.CHR, SNP=SNP , Position.SNP = Position_SNP)

################drawing the heatmap for one region ###########################
#tau = results$Region.List[1,2]: results$Region.List[1,3] 
#EXP.CNV =  results$EXP.corrected
#heatmap(EXP.CNV[tau,])

Results