Description Usage Arguments Value Author(s) Examples

Given a list of covariance matrices, this function simulate normal observation based on the covariance matrix from first until the last one in the list of cov.matrix

1 | ```
sample.dep.2(ngenes.dep, n, var.ratio, cov.matrix, delta, distrn = "normal")
``` |

`ngenes.dep` |
integer number of genes to sample the dependent expression |

`n` |
positive integer number of replicates for each gene each group (control/treatment) |

`var.ratio` |
a positive number for the ratio of variance between treatment and control |

`cov.matrix` |
a list of covariance matrices based on which normal samples are generated |

`delta` |
effect size for sample size calculation the absolute distance from null value for expression values of genes coming from the true alternative hypothesis group |

`distrn` |
distribution of expression data, set to be normal for here |

returns a matrix with ngenes.dep rows and 2*n columns with the first n columns being samples from control group and last n columns being from treatment group for each gene (row). each group of genes consists of subgroups of genes that are dependent

Peng Liu [email protected]

1 2 3 4 5 6 7 8 9 10 11 12 13 | ```
library(MASS)
n.dep <- 9; n <- 100; delta <- 5; var.ratio <- 1;
n.matrix <- 3; cov.matrix <- list(n.matrix);
for ( i in c(1:n.matrix)) {
cov.matrix[[i]] <- diag(rep(i*0.1, 3))
}
cov.matrix[[3]][1,1] <- cov.matrix[[2]][1,1] <- 1
cov.matrix[[3]][2,1] <- cov.matrix[[3]][1,2] <- 0.15
test.sample.dep <- sample.dep.2( n.dep , n, var.ratio, cov.matrix, delta, distrn = "normal")
``` |

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