sim.function: Simulation of multiple RNA-seq experiments

In metaRNASeq: Meta-Analysis of RNA-Seq Data

Description

Simulate data arising from multiple independent RNA-seq experiments

Usage

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

Arguments

param	Mean expression levels: param must be a data frame containing at least two columns named "mucond1" and "mucond2" and one row per gene.
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 is filled.
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, classes is built as a list of two vectors with nrep labels 1 (for condition 1) and nrep labels 2 (for condition 2).
inter.sd	Inter-study variability. By default, inter.sd is set to 0.3, which corresponds to a moderate inter-study variability in the case where param and dispFuncs parameters are used to simulate data.

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

## 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

Example output

     rep1 rep2 rep3 rep4 rep5 rep6 rep7 rep8
tag1  489  584  411  621  510  473  387  728
tag2  760  697  858 1000  756  520  814  951
tag3  134   66  184  130  147  146   75  118
tag4  288  259  164  250  501  590  389 1025
tag5    0    4    3    1    6    0    3    7
tag6  125  134   16   21   24   30   31   33
      study condition
rep1 study1 untreated
rep2 study1 untreated
rep3 study1 untreated
rep4 study1 untreated
rep5 study1   treated
rep6 study1   treated
rep7 study1   treated
rep8 study1   treated

metaRNASeq documentation built on Oct. 1, 2021, 5:07 p.m.