correct_with_ComBat {proBatch}R Documentation

Adjusts for discrete batch effects using ComBat

Description

Standardized input-output ComBat normalization ComBat allows users to adjust for batch effects in datasets where the batch covariate is known, using methodology described in Johnson et al. 2007. It uses either parametric or non-parametric empirical Bayes frameworks for adjusting data for batch effects. Users are returned an expression matrix that has been corrected for batch effects. The input data are assumed to be cleaned and normalized before batch effect removal.

Usage

correct_with_ComBat(data_matrix, sample_annotation,
  sample_id_col = "FullRunName", batch_col = "MS_batch",
  par.prior = TRUE)

Arguments

data_matrix

features (in rows) vs samples (in columns) matrix, with feature IDs in rownames and file/sample names as colnames. Usually the log transformed version of the original data

sample_annotation

data frame with sample ID, technical (e.g. MS batches) and biological (e.g. Diet) covariates

sample_id_col

name of the column in sample_annotation file, where the filenames (colnames of the data matrix are found)

batch_col

column in sample_annotation that should be used for batch comparison

par.prior

whether parametrical or non-parametrical prior should be used

Value

data_matrix-size data matrix with batch-effect corrected by ComBat

Examples

combat_corrected_matrix <- correct_with_ComBat(
example_proteome_matrix, example_sample_annotation)


[Package proBatch version 1.0.0 Index]