genMissingOmics: genMissingOmics

In ConesaLab/MultiBaC: Multiomic Batch effect Correction

Description

This function generates for all the batches the omic data they had not originally. This is the previous step to apply ARSyNbac [1] correction.

Usage

genMissingOmics(mbac)

Arguments

mbac	mbac object generated by *createMbac*. PLS models slot must be present.

Value

A list of *MultiAssayExperiment* structures. In this case, each batch contains all the omics introduced in MultiBaC. For instance, if two batches are being studying, "A" and "B", given that "A" contains "RNA-seq" and "GRO-seq" data and "B" contains "RNA-seq" and "Metabolomica" data, after applying *genMissingOmics* function batch "A" will contain "RNA-seq", "GRO-seq" and predicted "Metabolomics" data.

Examples

data('multiyeast')

my_mbac <- createMbac (inputOmics = list(A.rna, A.gro, B.rna, B.ribo, C.rna, C.par),
                       batchFactor = c("A", "A", "B", "B", "C", "C"),
                       experimentalDesign = list("A" =  c("Glu+", "Glu+",
                       "Glu+", "Glu-", "Glu-", "Glu-"),
                       "B" = c("Glu+", "Glu+", "Glu-", "Glu-"),
                       "C" = c("Glu+", "Glu+", "Glu-", "Glu-")),
                       omicNames = c("RNA", "GRO", "RNA", "RIBO", "RNA", "PAR"),
                       commonOmic = "RNA")

my_mbac_2 <- genModelList (my_mbac, test.comp = NULL,
                           scale = FALSE, center = TRUE,
                           crossval = NULL,
                           showinfo = TRUE)
multiBatchDesign <- genMissingOmics(my_mbac_2)

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