segmentation {SegCorr}

Correlation Matrix Segmentation

Description

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

\rho 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.(2017). SegCorr: a statistical procedure for the detection of genomic regions of correlated expression. BMC Bioinformatics, 18:333.

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)