runPipeline {MEAL} | R Documentation |
Wrapper for analysing differential methylation and expression at region and probe level.
runPipeline(set, variable_names, covariable_names = NULL, model = NULL, num_vars, sva = FALSE, betas = TRUE, range, region_methods = c("bumphunter", "blockFinder", "DMRcate"), verbose = FALSE, warnings = TRUE, DiffMean_params = NULL, DiffVar_params = list(coefficient = 1:2), bumphunter_params = NULL, blockFinder_params = NULL, dmrcate_params = NULL, rda_params = NULL)
set |
|
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 |
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 |
range |
|
region_methods |
Character vector with the methods used in |
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 |
DiffVar_params |
List with other parameter passed to |
bumphunter_params |
List with other parameter passed to |
blockFinder_params |
List with other parameter passed to |
dmrcate_params |
List with other parameter passed to |
rda_params |
List with other parameter passed to |
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.
ResultSet
object
if (require(minfiData)){ set <- ratioConvert(mapToGenome(MsetEx[1:10,])) res <- runPipeline(set, variable_names = "Sample_Group") res }