simulateGEdata: Simulate gene expression data.

In PeteHaitch/RUVcorr: Removal of unwanted variation for gene-gene correlations and related analysis.

Description

simulateGEdata returns simulated noisy gene expression values of specified size and its underlying gene-gene correlation.

Usage

simulateGEdata(n, m, k, size.alpha, corr.strength, g = NULL,
  Sigma.eps = 0.1, nc, ne, check.input = FALSE)

Arguments

n	An integer setting the number of genes.
m	An integer setting the number of arrays.
k	An integer setting number of dimensions of noise term, controls dimension of W and α.
size.alpha	A numeric scalar giving the maximal and minimal absolute value of α.
corr.strength	An integer controlling the dimension of X and β.
g	An integer value between [1, min(k, corr.strength)) giving the correlation between X and W or NULL for independence.
Sigma.eps	A numeric scalar setting the amount of random variation in ε; Sigma.eps >0.
nc	An integer setting the number of negative controls.
ne	An integer setting the number of strongly expressed genes.
check.input	A logical scalar; if TRUE all input is checked (not advisable for large simulations).

Details

This function generates log2-transformed expression values of n genes in m arrays. The expression values consist of true expression and noise:

Y=Xβ+Wα+ε

The dimensions of the matrices X and β are used to control the size of the correlation between the genes. It is possible to simualte three different classes of genes:

• correlated genes expressed with true log2-transformed values from 0 to 16

• correlated genes expressed with true log2-transformed values with mean 0

• uncorrelated genes with true log2-transformed expression equal to 0 (negative controls)

The negative control are always the last nc genes in the data, whereas the strongly expressed genes are always the first ne genes in the data. It is possible to either simulate data where W and X are independent by setting g to NULL, or increasing correlation bWX between W and X by increasing g.

Value

simulateGEdata returns output of the class simulateGEdata. An object of class simulateGEdata is a list with the following components:

• Truth A matrix containing the values of Xβ.

• Y A matrix containing the values in Y.

• Noise A matrix containing the values in Wα.

• Sigma A matrix containing the true gene-gene correlations, as defined by Xβ.

• Info A matrix containing some of the general information about the simulation.

Author(s)

Saskia Freytag

References

Laurent J., Gagnon-Bartsch J., Speed T. Correcting gene expression data when neither the unwanted variation nor the factor of interest are observed. Berkley Technical Reports (2012).

Examples

Y<-simulateGEdata(500, 500, 10, 2, 5, g=NULL, Sigma.eps=0.1, 250, 100, check.input=TRUE)
Y
Y<-simulateGEdata(500, 500, 10, 2, 5, g=3, Sigma.eps=0.1, 250, 100, check.input=TRUE)
Y

PeteHaitch/RUVcorr documentation built on May 8, 2019, 1:31 a.m.