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

1 2 3 | ```
generateExpressionData(fc = rep(0, 100),
Sigma.1 = diag(100), Sigma.2 = NULL, N.1 = 10,
N.2 = 10, use_cholesky = FALSE)
``` |

`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. |

`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. |

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

1 2 3 4 5 6 7 8 9 10 11 12 |

Questions? Problems? Suggestions? Tweet to @rdrrHQ or email at ian@mutexlabs.com.

Please suggest features or report bugs with the GitHub issue tracker.

All documentation is copyright its authors; we didn't write any of that.