SegCorr: Performs CNV Correction and Correlation Matrix Segmentation

In SegCorr: Detecting Correlated Genomic Regions

Description

Gene expression is corrected for CNV events must not contain NA's and genes with same expression value (i.e. null gene expression). Segmentation is used to detect changes in the correlation pattern. Regions with high correlation are identified using an exact test.

Usage

SegCorr(CHR, EXP, genes,S, CNV, SNPSMOOTH, Position.EXP, SNP.CHR, SNP, Position.SNP, Kmax)

Arguments

CHR	Chromosome allocation vector for the genes.
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.
CNV	Logical variable indicating whether to perform CNV correction. When CNV=T, the correction is performed. Default value CNV=F.
SNPSMOOTH	(Optional Argument when CNV=T) Logical variable indicating whether to perform SNPSMOOTH. When SNPSMOOTH=T, the smoothing is performed. Default value SNPSMOOTH=F.
Position.EXP	(Optional Argument when CNV=T) Expression position matrix. First column is the start position and the second is the end position.
SNP.CHR	(Optional Argument when CNV=T) Chromosome allocation vector for genomic probes.
SNP	(Optional Argument when CNV=T) SNP profile matrix not containing NA's. Columns correspond to patients and rows to probes.
Position.SNP	(Optional Argument when CNV=T) vector with SNP positions
Kmax	(Optional Argument when CNV=T and SNPSMOOTH=T) Maximum number of segments. (mean profile segmentation)

Details

Overlapping genes may correspond to the same genomic probes.

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	the region boundaries with repsect 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$genes	names of the genes belonging to the region
Results$p-valueadj	p-value of the region corrected for multiple testing
Chromosome.Inf	Matrix containing the estimated background correlation (rho0.hat) per chromsome, the number of segments and the log-loglikehood.
EXP.corrected	If the CNV option is chosen, the corrected signal is given.

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.

See Also

CNV_correction,segmentation

Examples

#data('EXP_raw')
#CHR = rep(1,dim(EXP_raw)[1])

#results = SegCorr(CHR = CHR, EXP = EXP_raw, CNV = FALSE,S=0.7)

################drawing the heatmap for one region ###########################
#tau = results$Region.List[1,2]: results$Region.List[1,3]
#heatmap(as.matrix(EXP_raw[tau,]))

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