segmentation: Correlation Matrix Segmentation

In SegCorr: Detecting Correlated Genomic Regions

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$Rho	ρ correlation
Results$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.(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)

Example output

Loading required package: cghseg
Loading required package: parallel

SegCorr documentation built on May 2, 2019, 6:01 a.m.