simulateAMR {ramr} | R Documentation

## Simulate a set of aberrantly methylated regions

### Description

'simulateAMR' returns a 'GRanges' object containing a set of randomly selected aberrantly methylated regions (AMRs) to be used as an input for the 'simulateData' method.

### Usage

    simulateAMR(
      template.ranges,
      nsamples,
      exclude.ranges = NULL,
      regions.per.sample = 1,
      samples.per.region = 1,
      sample.names = NULL,
      merge.window = 300,
      min.cpgs = 7,
      max.cpgs = Inf,
      min.width = 1,
      dbeta = 0.25
    )

### Arguments

`template.ranges` | A 'GRanges' object with genomic locations (same object must be supplied to this and to the 'simulateData' functions).
`nsamples` | A single integer >= 1 indicating the number of samples to which AMRs will be assigned.
`exclude.ranges` | A 'GRanges' object with genomic locations. None of the simulated AMRs in the output will overlap with any of regions from 'exclude.ranges'. If 'NULL' (the default), AMRs are not restricted by their genomic location.
`regions.per.sample` | A single integer >= 1 (the default). Number of AMRs to be assigned to every sample. Message is shown and the 'regions.per.sample' value is limited to 'maxnAMR (where 'maxnAMR' is the maximum number of potential AMRs for the 'template.ranges').
`samples.per.region` | A single integer >= 1 (the default). Number of samples to which the same AMR will be assigned. Message is shown and the 'samples.per.region' value is limited to 'nsamples' if the former is greater than the latter.
`sample.names` | A character vector with sample names. If 'NULL' (the default), sample names will be computed as 'paste0("sample", seq_len(nsamples))'. When specified, the length of the 'sample.names' vector must not be smaller than the value of 'nsamples'.
`merge.window` | A positive integer. All 'template.ranges' genomic locations within this distance will be merged to create a list of potential AMRs (which will be later filtered from regions overlapping with any regions from the 'exclude.ranges').
`min.cpgs` | A single integer >= 1. All AMRs containing less than 'min.cpgs' genomic locations are filtered out. The default: 7.
`max.cpgs` | A single integer >= 1. All AMRs containing more than 'max.cpgs' genomic locations are filtered out. The default: 'Inf'.
`min.width` | A single integer >= 1 (the default). Only AMRs with the width of at least 'min.width' are returned.
`dbeta` | A single non-negative numeric value in the range [0,1] or a numeric vector of such values (with as many elements as there are AMRs). Used to populate the 'dbeta' metadata column, defines a desired absolute deviation of corresponding AMR from the median for the 'simulateData' function.

### Details

Using provided template ('GRanges' object) 'simulateAMR' randomly selects genomic regions satisfying various criteria (number of CpGs, width of the region) and assigns them to samples according to specified parameters (number of AMRs per sample, number of samples per AMR). Its output is meant to be used as the set of true positive AMRs for the 'simulateData' function.

### Value

The output is a 'GRanges' object that contains a subset of aberrantly methylated regions (AMRs) randomly selected from all the possible AMRs for the provided 'template.ranges' object. The following metadata columns are included:

- 'revmap' – integer list of 'template.ranges' genomic locations that are included in this AMR region

- 'ncpg' – number of 'template.ranges' genomic locations within this AMR region

- 'sample' – an identifier of a sample to which corresponding AMR belongs

- 'dbeta' – equals to supplied 'dbeta' parameter

### See Also

`simulateData` for the generation of simulated test data sets, `getAMR` for identification of AMRs, `plotAMR` for plotting AMRs, `getUniverse` for info on enrichment analysis, and 'ramr' vignettes for the description of usage and sample data.

### Examples

      data(ramr)
      amrs.unique <-
        simulateAMR(ramr.data, nsamples=4, regions.per.sample=2,
                    min.cpgs=5, merge.window=1000, dbeta=0.2)
      amrs.nonunique <-
        simulateAMR(ramr.data, nsamples=3, exclude.ranges=amrs.unique,
                    samples.per.region=2, min.cpgs=5, merge.window=1000)

[Package ramr version 1.2.0 Index]