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,
  ...
)

gly_ancova_(expr_mat, groups, covariates, p_adj_method = "BH", ...)

Arguments

exp: A glyexp::experiment() object containing expression matrix and sample information.
group_col: (Only for gly_ancova()) A character string specifying the column name of the grouping variable in the sample information. Default is "group".
covariate_cols: (Only for gly_ancova()) 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. Only applicable to gly_ancova().
...: Additional arguments passed to stats::aov().
expr_mat: (Only for gly_ancova_()) A numeric matrix with variables as rows and samples as columns.
groups: (Only for gly_ancova_()) A factor or character vector specifying group membership for each sample. Must have at least 2 levels. Character vectors will be automatically converted to factors.
covariates: (Only for gly_ancova_()) A data frame, matrix, or vector of sample-level covariates. At least one covariate must be provided.

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

The function performs log2 transformation on the expression data (log2(x + 1)) before statistical testing. At least 2 groups and at least 1 covariate are required.

gly_ancova() is the top-level API that works with glyexp::experiment() objects and supports the add_info parameter for joining experiment metadata.

gly_ancova_() is the underlying API that works with matrices, factor vectors, and covariate data directly, providing more flexibility for users who don't use the glyexp package.

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.