runRegionAnalysis {MEAL} | R Documentation |
This function is a wrapper of two known region differentially methylated detection
methods: Bumphunter, blockFinder
and DMRcate.
runRegionAnalysis(set, model, methods = c("blockFinder", "bumphunter", "DMRcate"), coefficient = 2, bumphunter_params = NULL, blockFinder_params = NULL, dmrcate_params = NULL, verbose = FALSE, resultSet = TRUE)
set |
|
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 |
blockFinder_params |
List with other parameter passed to |
dmrcate_params |
List with other parameter passed to |
verbose |
Logical value. Should the function be verbose? (Default: FALSE) |
resultSet |
Should results be encapsulated in a |
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.
List or resultSet
with the result of the DMR detection methods.
bumphunter
, blockFinder
,
dmrcate
if (require(minfiData)){ set <- ratioConvert(mapToGenome(MsetEx[1:10,])) model <- model.matrix(~Sample_Group, data = pData(MsetEx)) res <- runRegionAnalysis(set, model) res }