Automatic Batch Correction

Detects and corrects batch effects in the experiment. If batch information is available, this function performs ANOVA to detect batch effects. If more than 30% (controlled by prop_threshold) of variables show significant batch effects (p < 0.05), batch correction is performed using ComBat. If group information exists, it will be used as a covariate in both detection and correction to preserve biological variation. If no batch information is available, the function will return the original experiment.

Usage

auto_correct_batch_effect(
  exp,
  group_col = "group",
  batch_col = "batch",
  prop_threshold = 0.3,
  check_confounding = TRUE,
  confounding_threshold = 0.4,
  info = NULL
)

Arguments

exp

A glyexp::experiment() object.

group_col

The column name in sample_info for groups. Default is "group". Can be NULL when no group information is available.

batch_col

The column name in sample_info for batches. Default is "batch". Can be NULL when no batch information is available.

prop_threshold

The proportion of variables that must show significant batch effects to perform batch correction. Default is 0.3 (30%).

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.

info

Internal parameter used by auto_clean().

Value

A glyexp::experiment() object with batch effects corrected.

Examples

exp <- glyexp::real_experiment
exp <- auto_correct_batch_effect(exp)
#> ℹ Batch column batch not found in sample_info. Skipping batch correction.