# Simulation of multiple RNA-seq experiments

### Description

Simulate data arising from multiple independent RNA-seq experiments

### Usage

1 | ```
sim.function(param, dispFuncs, nrep = 4, classes = NULL, inter.sd = 0.3)
``` |

### Arguments

`param` |
Mean expression levels: |

`dispFuncs` |
List of length equal to the number of studies to be simulated, containing the gamma regression parameters describing the mean-dispersion relationship for each one; these are the mean-dispersion functions linking mean and intra-study variability for each independent experiment. |

`nrep` |
Number of replicates to be simulated in each condition in each study. Ignored if |

`classes` |
List of class memberships, one per study (maximum 20 studies). Each vector or factor of the list can only
contain two levels which correspond to the two conditions studied. If NULL, |

`inter.sd` |
Inter-study variability. By default, |

### Details

Details about the simulation procedure are given in the following paper:

### Value

A matrix with simulated expression levels, one row per gene and one column per replicate. Names of studies are given in the column names of the matrix.

### Note

If the `param`

data provided in this package are not used to simulate data, one should check that the
per-condition means in `param`

are reasonable. Note also that for genes to be simulated as non-differentially
expressed, the values of "mucond1" and "mucond2" in `param`

should be equal.

### References

A. Rau, G. Marot and F. Jaffrezic (2014). Differential meta-analysis of RNA-seq data. *BMC Bioinformatics* **15**:91

### See Also

`metaRNASeq`

### Examples

1 2 3 4 5 6 7 8 9 10 11 12 13 | ```
## Load simulation parameters
data(param)
data(dispFuncs)
## Simulate data
matsim <- sim.function(param = param, dispFuncs = dispFuncs)
sim.conds <- colnames(matsim)
rownames(matsim) <-paste("tag", 1:dim(matsim)[1],sep="")
# extract simulated data from one study
simstudy1 <- extractfromsim(matsim,"study1")
head(simstudy1$study)
simstudy1$pheno
``` |