ARSyNbac: ARSyNbac

In ConesaLab/MultiBaC: Multiomic Batch effect Correction

Description

ARSyNbac

Usage

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

Arguments

mbac	mbac object generated by *createMbac*.
batchEstimation	Logical. If TRUE (default) the batch effect is estimated and used to correct the data. Use TRUE when the source of the batch effect is known.
filterNoise	Logical. If TRUE (default) structured noise is removed form residuals. Use this option when there is an unknown source of batch effect in data.
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 explained_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.

Value

Custom mbac object. Elements in a mbac object:

1. ListOfBatches: A list of MultiAssayExperiment objects (one per batch).

2. 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.

3. CorrectedData: Same structure than ListOfBatches but with the corrected data instead of the original.

4. ARSyNmodels: ARSyN models created during MultiBaC performance (one per omic data type).

References

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.

Examples

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, filterNoise = TRUE,
                  Interaction=TRUE, Variability = 0.90, beta = 2,
                  modelName = "Model 1",
                  showplot = FALSE)

ConesaLab/MultiBaC documentation built on Jan. 24, 2022, 5:17 a.m.