DAPipeline: Perform differential methylation analysis

Description

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

Usage

DAPipeline(set, variable_names, variable_types = rep(NA, length(variable_names)), covariable_names = NULL, covariable_types = rep(NA, length(covariable_names)), equation = NULL, num_var = NULL, labels = NULL, sva = FALSE, region_methods = c("bumphunter", "DMRcate"), shrinkVar = FALSE, probe_method = "ls", max_iterations = 100, num_feat = 50, num_cores = 1, verbose = FALSE, ...)

Arguments

set

MethylationSet or ExpressionSet

variable_names

Character vector with the names of the variables that will be returned as result.

variable_types

Character vector with the types of the variables. As default, variables type won't be changed.

covariable_names

Character vector with the names of the variables that will be used to adjust the model.

covariable_types

Character vector with the types of the covariables. As default, variables type won't be changed.

equation

Character containing the formula to be used to create the model.

num_var

Numeric with the number of variables in the matrix for which the analysis will be performed. Compulsory if equation is not null.

labels

Character vector with the labels of the variables.

sva

Logical indicating if Surrogate Variable Analysis should be applied.

region_methods

Character vector with the methods used in DARegion. If "none", region analysis is not performed.

shrinkVar

Logical indicating if shrinkage of variance should be applied in probe analysis.

probe_method

Character with the type of linear regression applied in probe analysis ("ls" or "robust")

max_iterations

Numeric with the maximum of iterations in the robust regression.

num_feat

Numeric with the minimum number of cpg beta values to be included in the results.

num_cores

Numeric with the number of cores to be used.

verbose

Logical value. If TRUE, it writes out some messages indicating progress. If FALSE nothing should be printed.

...

Further arguments passsed to DARegion function.

Value

MethylationResult object

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.

Examples

Run this code

if (require(minfiData)){
 set <- prepareMethylationSet(matrix = getBeta(MsetEx)[1:10, ], pheno = pData(MsetEx))
 res <- DAPipeline(set, variable_names = "Sample_Group", probe_method = "ls") 
 res
}