Correct Batch Effect — correct_batch

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

Usage

correct_batch_effect(
  x,
  batch = "batch",
  group = NULL,
  check_confounding = TRUE,
  confounding_threshold = 0.4
)

# S3 method for class 'glyexp_experiment'
correct_batch_effect(
  x,
  batch = "batch",
  group = NULL,
  check_confounding = TRUE,
  confounding_threshold = 0.4
)

# S3 method for class 'matrix'
correct_batch_effect(
  x,
  batch = "batch",
  group = NULL,
  check_confounding = TRUE,
  confounding_threshold = 0.4
)

# Default S3 method
correct_batch_effect(
  x,
  batch = "batch",
  group = NULL,
  check_confounding = TRUE,
  confounding_threshold = 0.4
)

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.
check_confounding: Whether to check for confounding between batch and group variables. Default to TRUE.
confounding_threshold: The threshold for Cramer's V to consider batch and group variables highly confounded. Only used when check_confounding is TRUE. Default to 0.4.

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 (|Cramer's V| > threshold), 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")
#> Found2batches
#> Adjusting for1covariate(s) or covariate level(s)
#> Standardizing Data across genes
#> Fitting L/S model and finding priors
#> Finding parametric adjustments
#> Adjusting the Data

# With matrix and factor vectors
mat <- matrix(abs(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)
#> Found2batches
#> Adjusting for1covariate(s) or covariate level(s)
#> Standardizing Data across genes
#> Fitting L/S model and finding priors
#> Finding parametric adjustments
#> Adjusting the Data