For a given chromosome, gene correlation matrix segmentation is performed. Regions with high
correlation are identified using an exact test. The expression matrix must not contain NA's and genes with same expression value (i.e. null gene expression).
Usage
segmentation(CHR, EXP, genes, S)
Arguments
CHR
chromosome name
EXP
Gene expression matrix (raw/corrected for CNV). Columns correspond to patients and rows to genes. The expression matrix must not contain either NA's or genes with same expression value (i.e. null gene expression).
genes
Gene ID(name) vector.
S
Threshold for model selection. Default S=0.7.
Value
Results
Matrix containing information about the genomic regions. Each region corresponds to a row of the matrix, the one with the smallest p-value is on the top of the list.
Results$CHR
Chromosome
Results$Start/End
region boundaries with respect to the physical location of the gene in the chromosome
Results$Rho
ρ correlation
Results$length
number of genes in the region
Results$first/last gene
name of the first/last gene in the region
Results$p-value
p-value as obtained from the test
Results$genes
names of genes belonging to the region
rho0
estimate of the background correlation
likelihood
log-likelihood
K
number of segments
Author(s)
E. I. Delatola, E. Lebarbier, T. Mary-Huard, F. Radvanyi, S. Robin, J. Wong.
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.
Examples
#data(EXP_raw)
#G = cor(t(EXP_raw))## calculating the gene x gene correlation matrix
#image(G)## plotting the correlation matrix
#results = segmentation(EXP = EXP_raw)
providing 
.