runRegionAnalysis {MEAL} | R Documentation

## Run different DMR detection methods

### Description

This function is a wrapper of two known region differentially methylated detection methods: Bumphunter, `blockFinder` and DMRcate.

### Usage

    runRegionAnalysis(set, model, methods = c("blockFinder", "bumphunter",
      "DMRcate"), coefficient = 2, bumphunter_params = NULL,
      blockFinder_params = NULL, dmrcate_params = NULL, verbose = FALSE,
      resultSet = TRUE)

### Arguments

`set` | `GenomicRatioSet`, `eSet` derived object or `SummarizedExperiment`
`model` | Model matrix representing a linear model.
`methods` | Character vector with the names of the methods used to estimate the regions. Valid names are: "blockFinder", "bumphunter" and "DMRcate".
`coefficient` | Numeric with the index of the model matrix used to perform the analysis.
`bumphunter_params` | List with other parameter passed to `runBumphunter` function.
`blockFinder_params` | List with other parameter passed to `runBlockFinder` function.
`dmrcate_params` | List with other parameter passed to `runDMRcate` function.
`verbose` | Logical value. Should the function be verbose? (Default: FALSE)
`resultSet` | Should results be encapsulated in a `resultSet`? (Default: TRUE)

### Details

`runRegionAnalysis` performs a methylation region analysis using bumphunter, `blockFinder` and DMRcate. Bumphunter allows the modification of several parameters that should be properly used.

Cutoff will determine the number of bumps that will be detected. The smaller the cutoff, the higher the number of positions above the limits, so there will be more regions and they will be greater. `Bumphunter` can pick a cutoff using the null distribution, i.e. permutating the samples. There is no standard cutoff and it will depend on the features of the experiment. Permutations are used to estimate p-values and, if needed, can be used to pick a cutoff. The advised number of permutation is 1000. The number of permutations will define the maximum number of bumps that will be considered for analysing. The more bumps, the longer permutation time. As before, there is not an accepted limit but `minfi` tutorial recommends not to exceed 30000 bumps. Finally, if supported, it is very advisable to use parallelization to perform the permutations.

Due to `minfi` design, BlockFinder can only be run using own minfi annotation. This annotation is based on hg19 and Illumina 450k chipset. Cpg sites not named like in this annotation package will not be included. As a result, the use of BlockFinder is not recommended.

DMRcate uses a first step where linear regression is performed in order to estimate coefficients of the variable of interest. This first step is equal to the calculation performed in `DAProbe`, but using in this situation linear regression and not robust linear regression.

### Value

List or `resultSet` with the result of the DMR detection methods.

### See Also

`bumphunter`, `blockFinder`, `dmrcate`

### Examples

    if (require(minfiData)){
    set <- ratioConvert(mapToGenome(MsetEx[1:10,]))
     model <- model.matrix(~Sample_Group, data = pData(MsetEx))
     res <- runRegionAnalysis(set, model)
     res
    }

[Package MEAL version 1.16.0 Index]