CNV_correction {SegCorr}

Corrects Gene Expression for CNV

Description

Correcting gene expression signal for CNV.

Usage

CNV_correction(s.Position.EXP, e.Position.EXP, Position.SNP, mu.SNP, EXP)

Arguments

s.Position.EXP

vector with gene start position

e.Position.EXP

vector with gene end position

Position.SNP

vector with SNP/CGH positions

mu.SNP

Smoothed genomic signal matrix not containing NA values. Rows correspond to probes, while columns to patients. The ordering of the patients must be the same as in the EXP matrix.

EXP

Gene expression matrix must not contain NA's and genes with same expression value (i.e. null gene). Rows correspond to probes, while columns to patients. Again, ordering of patients must be the same between EXP and mu.SNP matrices.

Details

Overlapping genes may correspond to the same SNP/CGH probes.

Value

CNV corrected signal matrix.

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

segmented_signal

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)
#mu.SNP = segmented_signal(SNP ,100) ## smoothed SNP signal
#EXP.CNV = CNV_correction(Position_EXP[,1], Position_EXP[,2], Position_SNP,
#mu.SNP, EXP_raw)## corrected signal