generateData: Simulator for gene expression data
In RDRToolbox: A package for nonlinear dimension reduction with Isomap and LLE.

Description Usage Arguments Details Value Author(s) Examples

View source: R/generateData.R

A simulator for gene expression data, whose values are normally distributed values with zero mean. The covariances are given by a configurable block-diagonal matrix. By default, half of the samples contain differential gene expression values (see parameter diffsamples).

1	generateData(samples=50, genes=10000, diffgenes=200, blocksize=50, cov1=0.2, cov2=0, diff=0.6, diffsamples)

`samples`	number of samples
`genes`	number of gene expression values per sample
`diffgenes`	number of differential genes for class 1
`blocksize`	size of each block in the blockdiagonal correlation matrix
`cov1`	covariance within the blocks in the correlation matrix
`cov2`	covariance between the blocks in the correlation matrix
`diff`	difference between the random gene expression values and the differential gene expression values
`diffsamples`	number of samples containing differential gene expression values compared to the rest (if missing, this parameter is set to half of the total number of samples)

The simulator generates two labeled classes:
label 1: samples with differentially expressed genes.
label -1: samples without differentially expressed genes.

'generateData' returns a list containing:

`data`	a (samples x features)-matrix with the simulated gene expression values
`labels`	a vector with labels (1,-1) for the two classes

Christoph Bartenhagen

## generate a dataset with 20 samples and 1.000 gene expression values
d = generateData(samples=20, genes=1000, diffgenes=100, blocksize=10)
data = d[[1]]
labels = d[[2]]