ARSyNbac: ARSyNbac
In MultiBaC: Multiomic Batch effect Correction

Description Usage Arguments Value References Examples

ARSyNbac

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ARSyNbac(mbac, batchEstimation = TRUE, Interaction = FALSE,
  Variability = 0.9, beta = 2, modelName = "Model 1",
  showplot = TRUE)

`mbac`	mbac object generated by createMbac.
`batchEstimation`	Logical. If TRUE (default) the batch effect is estimated and used to correct the data. If batch effect is unknown or it is not the main source of noise, this argument must be set to FALSE and ARSyNbac will extract unwanted effects from residuals.
`Interaction`	Logical. Whether to model the interaction between factors or not (FALSE by default).
`Variability`	From 0 to 1. Minimum percent of data variability that must be explained by each model. By default, 0.90.
`beta`	Numeric. Components that represent more than beta times the average variability are identified as systematic noise in residuals. Used in noise reduction mode. By default, 2.
`modelName`	Name of the model created. This name will be showed if you use the explaine_var plot function. By default, "Model 1".
`showplot`	Logical. If TRUE (default), the explained_var plot is showed. This plot represents the number of components selected for the ARSyN model.

Custom mbac object. Elements in a mbac object:

ListOfBatches: A list of MultiAssayExperiment objects (one per batch).
commonOmic Name of the common omic between the batches. It must be one of the names in omicNames argument. If NULL (default), the omic names that appears more times is selected as commonOmic.
CorrectedData: Same structure than ListOfBatches but with the corrected data instead of the original.
ARSyNmodels: ARSyN models created during MultiBaC performance (one per omic data type).

Nueda MJ, Ferrer A, Conesa A. ARSyN: A method for the identification and removal of systematic noise in multifactorial time course microarray experiments. Biostatistics. 2012;13:553–66.

data('multiyeast')

my_mbac <- createMbac (inputOmics = list(A.rna, B.rna, C.rna),
                       batchFactor = c("A", "B", "C"),
                       experimentalDesign = list("A" =  c("Glu+",
                       "Glu+", "Glu+", "Glu-",
                       "Glu-", "Glu-"),
                       "B" = c("Glu+", "Glu+", "Glu-", "Glu-"),
                       "C" = c("Glu+", "Glu+", "Glu-", "Glu-")),
                       omicNames = "RNA")
my_final_mbac <- ARSyNbac (my_mbac, batchEstimation = TRUE,
                  Interaction=TRUE, Variability = 0.90, beta = 2,
                  modelName = "Model 1",
                  showplot = FALSE)