DiffCor: Differential correlation analysis

In SCBIT-YYLab/DysRegSig: Gene dysregulation analysis and construction of mechanistic signature in cancer

Description

Differential correlation analysis with Fishers' Z test.

Usage

DiffCor(exp.1, exp.2, tf2tar, cor.method = 'pearson', p.adj, verbose = TRUE)

Arguments

exp.1	Expression matrix of a special condition. Columns correspond to genes, rows correspond to experiments. The matrix is expected to be already normalized.
exp.2	Expression matrix of an another special condition. Columns correspond to genes, rows correspond to experiments. The matrix is expected to be already normalized.
tf2tar	The prior reference GRN containing TF-target relationships.
cor.method	The method used to calculate correlation coefficient.
p.adj	Correction method for p value adjust.
verbose	A logical value indicating whether display the computating progress.

Value

The identified regulations.

Examples

data(ExpData) 
data(tf2tar)
data(ClinData)

group.1 <- ClinData$sample[which(ClinData$binaryResponse == 'CR/PR')]
exp.1 <- ExpData[,colnames(ExpData) %in% group.1]

group.2 <- ClinData$sample[which(ClinData$binaryResponse == 'SD/PD')]
exp.2 <- ExpData[,colnames(ExpData) %in% group.2]

## implement differential correlation analysis
diffcor.res <- DiffCor(exp.1 = exp.1, exp.2 = exp.2, tf2tar, 
                       cor.method = 'pearson', p.adj = 'BH',
                       verbose = TRUE)
                       
## set cutoff
diffcor.res <- subset(diffcor.res,p.val < 0.05)
head(diffcor.res)

SCBIT-YYLab/DysRegSig documentation built on July 19, 2021, 4:38 a.m.