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 1.10.1 Index]