Analysis of Covariance (ANCOVA) for Differential Expression Analysis

Perform ANCOVA for glycomics or glycoproteomics data with sample-level covariates. The function supports parametric comparison of multiple groups while adjusting for covariates. For significant results, emmeans post-hoc comparisons (Tukey adjustment) are automatically performed. P-values are adjusted for multiple testing using the method specified by p_adj_method.

Usage

gly_ancova(
  exp,
  group_col = "group",
  covariate_cols = NULL,
  p_adj_method = "BH",
  add_info = TRUE,
  ...,
  log_transform = TRUE
)

Arguments

exp: A glyexp::GlycomicSE() or glyexp::GlycoproteomicSE() object, or another SummarizedExperiment containing an expression matrix and sample information.
group_col: A character string specifying the column name of the grouping variable in the sample information. Default is "group".
covariate_cols: A character vector specifying column names in sample information to include as covariates. At least one covariate must be provided.
p_adj_method: A character string specifying the method to adjust p-values. See p.adjust.methods for available methods. Default is "BH". If NULL, no adjustment is performed.
add_info: A logical value. If TRUE (default), variable information from the experiment will be added to the result tibble. If FALSE, only the statistical results are returned.
...: Additional arguments passed to stats::aov().
log_transform: A single non-missing logical value. If TRUE (default), transform expression values using log2(x + 1e-6) before analysis. If FALSE, use the supplied values unchanged, for example for already log-transformed data.

Value

A list containing two elements:

tidy_result: A list containing:
- main_test: A tibble with ANCOVA results containing the following columns:
  - variable: Variable name
  - term: ANCOVA term (usually "group")
  - df: Degrees of freedom
  - sumsq: Sum of squares
  - meansq: Mean squares
  - statistic: F-statistic
  - p_val: Raw p-value from ANCOVA
  - p_adj: Adjusted p-value (if p_adj_method is not NULL)
  - post_hoc: Significant group pairs from post-hoc test, in the format of "ref_vs_test".
- post_hoc_test: A tibble with pairwise comparison results containing the following columns:
  - variable: Variable name
  - ref_group: Reference group
  - test_group: Test/treatment/case group
  - p_val: Adjusted p-value from emmeans pairwise test
  - p_adj: Adjusted p-value from emmeans pairwise test
raw_result: A list containing:
- main_test: A list of raw aov model objects.
- post_hoc_test: A list of raw emmeans results.

Details

Tests and effect sizes use the selected analysis scale. Reported fold changes continue to use ratios of group means from the original supplied values.

By default, expression data are transformed using log2(x + 1e-6) before statistical testing. Set log_transform = FALSE to skip this transformation. At least 2 groups and at least 1 covariate are required.

For any variable failed to fit a stats::aov() model, NAs will be assigned to the results in both main test and post-hoc test.

Post-hoc Test: emmeans pairwise comparisons with Tukey adjustment (emmeans::emmeans()) are performed for variables with significant main effects (p_adj < 0.05).

Required packages

This function requires the emmeans package for post-hoc comparisons.