Correct Batch Effect

Correct batch effects in glycoproteomics/glycomics data using ComBat algorithm from the sva package.

Usage

correct_batch_effect(x, batch = "batch", group = NULL)

Arguments

x

Either a glyexp_experiment object or a matrix. If a matrix, rows should be variables and columns should be samples.

batch

Either a factor/character vector specifying batch assignments for each sample, or a string specifying the column name in sample_info (for experiment input only). Default to "batch" for experiment input.

group

Either a factor/character vector specifying group assignments for each sample, or a string specifying the column name in sample_info (for experiment input only). If provided, it will be used as a covariate in the ComBat model. This is useful when you have an unbalanced design. Default to NULL.

Value

For glyexp_experiment input, returns a modified glyexp_experiment object. For matrix input, returns a batch-corrected matrix.

Details

This function performs batch effect correction using the ComBat algorithm. It requires batch information provided via the batch parameter. If no batch information is available, the function will return the original data unchanged.

If group information is provided via group, the function will check for confounding between batch and group variables. If batch and group are highly confounded (complete overlap), the function will issue a warning and return the original data unchanged to avoid over-correction.

When both batch and group information are available and not highly confounded, the group information will be included in the model to preserve biological variation while correcting for batch effects.

Examples

# With glyexp_experiment and column names
exp <- glyexp::toy_experiment()
exp$sample_info$batch <- c("A", "A", "A", "B", "B", "B")
exp$sample_info$group <- c("Ctrl", "Ctrl", "Treat", "Ctrl", "Treat", "Treat")
corrected_exp <- correct_batch_effect(exp, batch = "batch", group = "group")
#> ✔ Batch effect correction completed using ComBat algorithm.

# With matrix and factor vectors
mat <- matrix(rnorm(200), nrow = 20, ncol = 10)
batch_factor <- factor(rep(c("A", "B"), each = 5))
group_factor <- factor(rep(c("Ctrl", "Treat"), times = 5))
corrected_mat <- correct_batch_effect(mat, batch = batch_factor, group = group_factor)
#> Warning: NaNs produced
#> ✔ Batch effect correction completed using ComBat algorithm.