generateExpressionData: Generate Gene Expression Data

Description Usage Arguments Value Author(s) See Also Examples

Description

This function simulates gene expression data based on the multivariate normal distribution for two groups of samples.

Usage

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  generateExpressionData(fc = rep(0, 100),
    Sigma.1 = diag(100), Sigma.2 = NULL, N.1 = 10,
    N.2 = 10, use_cholesky = FALSE)

Arguments

fc

the vector of foldchanges between the two groups

Sigma.1

the covariance matrix describing the correlation between the genes in group one

Sigma.2

the covariance matrix describing the correlation between the genes in group two. If this is NULL, the case of equal covariances is assumed and Sigma.2 is set to Sigma.1.

N.1

the sample size of group one

N.2

the sample size of group two

use_cholesky

this is a boolean parameter that indicates whether the covariance matrices are cholesky decomposed. This is an enourmous speed up when simulating.

Value

X.1

the simulated gene expression levels of group one

X.2

the simulated gene expression levels of group two

d

the dimension, i.e. the number of genes

fc

the fold change vector. This is the unchanged parameter to the function.

Author(s)

Andreas Leha andreas.leha@med.uni-goettingen.de

See Also

generatePatientData

Examples

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## create a vector of fold changes
fc <- c(rep(0, 500), ceiling(rnorm(500, 0, 1)-0.5))

## uncorrelated genes
Sigma.1 <- diag(1000)
Sigma.2 <- diag(1000)

## the sample sizes
N.1 <- 30
N.2 <- 30

## Not run: G <- generateExpressionData(fc, Sigma.1, Sigma.2, N.1, N.2)


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