dataSim {methylKit} | R Documentation

## Simulate DNA methylation data

### Description

The function simulates DNA methylation data from multiple samples. See references for detailed explanation on statistics.

### Usage

    dataSim(replicates, sites, treatment, percentage = 10, effect = 25,
      alpha = 0.4, beta = 0.5, theta = 10, covariates = NULL,
      sample.ids = NULL, assembly = "hg18", context = "CpG",
      add.info = FALSE)

### Arguments

`replicates` | the number of samples that should be simulated.
`sites` | the number of CpG sites per sample.
`treatment` | a vector containing treatment information.
`percentage` | the proportion of sites which should be affected by the treatment.
`effect` | a number between 0 and 100 specifying the effect size of the treatment. This is essentially describing the average percent methylation difference between differentially methylated bases.See 'Examples' and 'Details'.
`alpha` | shape1 parameter for beta distribution (used for initial sampling of methylation proportions)
`beta` | shape2 parameter for beta distribution (used for initial sampling of methylation proportions)
`theta` | dispersion parameter for beta distribution (initial sampling of methylation proportions)
`covariates` | a data.frame containing covariates (optional)
`sample.ids` | will be generated automatically from `treatment`, but can be overwritten by a character vector containing sample names.
`assembly` | the assembly description (e.g. "hg18").Only needed for book keeping.
`context` | the experimanteal context of the data (e.g. "CpG"). Only needed for book keeping.
`add.info` | if set to TRUE, the output will be a list with the first element being the methylbase object and a vector of indices that indicate which CpGs should be differentially methylated. This vector can be used to subset simulated methylBase or methylDiff object with differentially methylated bases.

### Value

a methylBase object containing simulated methylation data, or if add.info=TRUE a list containing the methylbase object and the indices of all treated sites (differentially methylated bases or regions) as the second element.

### Details

The function uses a Beta distribution to simulate the methylation proportion background across all samples. The parameters `alpha`, `beta` used in a beta distribution to draw methylation proportions,μ, from a typical bimodal distribution. For each initial methylation proportion drawn using the parameters above, a range of methylation proportions is distributed around the original μ with overdispersion parameter θ, this is using an alternative parameterization of Beta distribution: Beta(μ,θ). The parameters `percentage` and `effect` determine the proportion of sites that are affected by the treatment (meaning differential sites) and the strength of this influence, respectively. `effect` is added on top of μ for the CpGs that are affected by the treament. The affected group of samples for that particular CpG will now be distributed by Beta(μ+effect,θ). The coverage is modeled with a negative binomial distribution, using `rnbinom` function with `size=1` and `prob=0.01`. The additional information needed for a valid methylBase object, such as CpG start, end and strand, is generated as "dummy values", but can be overwritten as needed.

### Examples

    data(methylKit)

    # Simulate data for 4 samples with 20000 sites each.
    # The methylation in 10% of the sites are elevated by 25%.
    my.methylBase=dataSim(replicates=4,sites=2000,treatment=c(1,1,0,0),
    percentage=10,effect=25)

[Package methylKit version 1.12.0 Index]