runPipeline {MEAL} | R Documentation

## Perform differential methylation analysis

### Description

Wrapper for analysing differential methylation and expression at region and probe level.

### Usage

    runPipeline(
      set,
      variable_names,
      covariable_names = NULL,
      model = NULL,
      weights = NULL,
      num_vars,
      sva = FALSE,
      betas = TRUE,
      range,
      analyses = c("DiffMean"),
      verbose = FALSE,
      warnings = TRUE,
      DiffMean_params = NULL,
      DiffVar_params = list(coefficient = 1:2),
      rda_params = NULL,
      method = "ls"
    )

### Arguments

`set` | `GenomicRatioSet`, `eSet` derived object or `SummarizedExperiment`
`variable_names` | Character vector with the names of the variables that will be returned as result.
`covariable_names` | Character vector with the names of the variables that will be used to adjust the model.
`model` | Model matrix or formula to get model matrix from `set`.
`weights` | weights used in the lmFit model (default NULL)
`num_vars` | Numeric with the number of variables in the matrix for which the analysis will be performed. Compulsory if equation is not null.
`sva` | Logical. Should Surrogate Variable Analysis be applied? (Default: FALSE)
`betas` | If `set` is a `GenomicRatioSet`, should beta values be used? (Default: TRUE)
`range` | `GenomicRanges` with the region used for RDA
`analyses` | Vector with the names of the analysis to be run (DiffMean and/or DiffVar).
`verbose` | Logical value. If TRUE, it writes out some messages indicating progress. If FALSE nothing should be printed.
`warnings` | Should warnings be displayed? (Default:TRUE)
`DiffMean_params` | List with other parameter passed to `runBumphunter` function.
`DiffVar_params` | List with other parameter passed to `runBumphunter` function.
`rda_params` | List with other parameter passed to `runRDA` function.
`method` | String indicating the method used in the regression: "ls" or "robust". (Default: "ls")

### Details

This function is the main wrapper of the package. First, it simplifies the the set to only contain the common samples between phenotype and features. In addition, it allows to change the class of the variables and to apply genomic models (more information on `preparePhenotype`). Afterwards, analysis per probe and per region are done merging the results in an `AnalysisResults` object.

Default linear model will contain a sum of the variables and covariables. If interactions are desired, a costum formula can be specified. In that case, variables and covariables must also be specified in order to assure the proper work of the resulting `AnalysisResult`. In addition, the number of variables of the model for which the calculation will be done must be specified.

### Value

`ResultSet` object

### Examples

    if (require(minfiData)){
    set <- ratioConvert(mapToGenome(MsetEx[1:10,]))
     res <- runPipeline(set, variable_names = "Sample_Group")
     res
    }

[Package MEAL version 1.20.4 Index]