# DEA options for RNA-seq count simulations in two-group comparison.

Source: `R/Setup.R`
`SimSetup.Rd`

This function adds user provided options for simulating RNA-seq data to RNAseq.SimSetup object. The resulting output list object is the input for `simulateDE` function.

    SimSetup(desetup,
    params,
    spike=NULL,
    size.factors='equal',
    downsample=FALSE,
    geneset=FALSE)

## Arguments

desetup | The RNAseq simulation parameters created by `DESetup`.
params | The negative binomial parameters for simulations. This can be: (1) The output of `estimateParam`. (2) A string specifying the name of precalculated estimates, see details.
spike | The spike-in simulation parameters created by `estimateSpike`. Default is `NULL`. These are needed for applying spike-in-dependent normalisation methods, i.e. 'RUV' and 'BASiCS'.
size.factors | Size factors representing sample-specific differences/biases in expected mean values of the NB distribution: "equal" or "given". The default is "equal", i.e. equal size factor of 1. If the user defines it as given, the size factors are sampled from the size factors provided by the output of `estimateParam`.
downsample | Drawing the associated dispersions after determining effective mean expressions by size factors. Default is `FALSE`.
geneset | Sampling with replacement or filling count tables with low magnitude Poisson when the estimated mean expression vector is shorter than the number of genes to be simulated. Default is `FALSE`, i.e. random sampling of mean expression values with replacement.

## Value

A list with the following entries:

desetup

  The RNAseq simulation parameters.

params

  The distributional parameters for simulations of genes.

spike

  The distributional parameters for simulations of spike-ins.

size.factors

  Size factor definition: "equal" means no difference in size factors between samples. "given" means that the size factors will be randomly drawn from the size factors provided by `params`. The user can also provide a list object containing sampling distributions per group (n1 and n2). Defaul is `"equal"`.

## Examples

    # NOT RUN {
    ## Setting DE options
    de.opts <- DESetup(ngenes = 10000, nsims = 25,
    p.DE = 0.2, pLFC = function(x) sample(c(-1,1), size=x,replace=TRUE)*rgamma(x, 3, 3),
    p.B=0.1, bLFC = function(x) rnorm(x, mean=0, sd=1.5), bPattern="uncorrelated",
    sim.seed = 43856)
    ## Combining DE options with parameters
    sim.opts <- SimSetup(desetup = de.opts,
    params = kolodziejczk_param,
    spike=NULL, size.factors = "equal",
    downsample = FALSE, geneset = FALSE)
    # }