CORREP (version 1.38.0)

cor.balance: Multivariate Correlation Estimator (Equal Number of Replicates)

Description

cor.balance estimates correlation matrix from replicated data assuming equal number of replicates. The data must be formatted in the right format (rows correspond to replicates, columns correspond to conditions, see example below) and the variance of each row of the data MUST equal to 1 (see example below).

Usage

cor.balance(x, m, G)

Arguments

x

data matrix, column represents samples (conditions), and row represents variables (genes), see example below for format information

m

number of replicates for each variable (gene)

G

number of variables (genes)

Value

A correlation matrix estimated for G variables (genes)

Details

The multivariate correlation estimator assumes replicated omics data are iid samples from the multivariate normal distribution. It is derived by maximizing the likelihood function. Note that each off-diagonal element in the returned correlation matrix (G by G) is the average of off-diagonals of MLE of correlation matrix of a pair of variables (m by m).

References

Zhu, D and Li Y. 2007. Multivariate Correlation Estimator for Inferring Functional Relationships from Replicated 'OMICS' data. Submitted.

See Also

cor.balance, cor

Examples

library("CORREP")
d0 <- NULL
for(l in 1:10)
d0 <- rbind(d0, rnorm(100))
## The simulated data corresponds to the real-world data of 25 genes and 10 conditions, each gene expression
## profiles was replicated 4 times.
d0<- t(d0)
## This step is to make the standard deviation of each replicate equals to 1
## so that we can model the covariance matrix as correlation matrix.
d0.std <- apply(d0, 1, function(x) x/sd(x))
M <- cor.balance(t(d0.std), m=4, G=25)