Compare peptide abundances between conditions

Performs differential abundance analysis on proteomics data. Supports three methods: GLM (default), ART (non-parametric), and pairwise tests.

Usage

compare(data, ...)

# S3 method for class 'pepdiff_data'
compare(
  data,
  compare,
  ref,
  within = NULL,
  method = c("glm", "art", "pairwise"),
  test = c("wilcoxon", "bootstrap_t", "bayes_t", "rankprod"),
  alpha = 0.05,
  fdr_method = "BH",
  bf_threshold = 3,
  ...
)

Arguments

data: A pepdiff_data object from [read_pepdiff()]
...: Additional arguments passed to methods
compare: Factor to compare (character string)
ref: Reference level for comparisons
within: Optional factor(s) to stratify by
method: Analysis method: "glm" (default), "art", or "pairwise"
test: For pairwise method: "wilcoxon", "bootstrap_t", "bayes_t", or "rankprod"
alpha: Significance threshold (default 0.05). Used for p-value based tests.
fdr_method: FDR correction method (default "BH"). Not applied for bayes_t.
bf_threshold: Bayes factor threshold for significance (default 3). Only used when test = "bayes_t". BF > threshold marks peptide as significant.

Value

A pepdiff_results object containing:

results: Tibble with peptide, gene_id, comparison, fold_change, log2_fc, p_value, fdr, significant. For bayes_t: p_value/fdr are NA, includes bf and evidence columns.
comparisons: Tibble defining the comparisons made
method: Statistical method used
diagnostics: Model convergence information (for GLM/ART)
params: Analysis parameters
data: The original pepdiff_data object
call: The function call

Examples

if (FALSE) { # \dontrun{
# Simple comparison
results <- compare(data, compare = "treatment", ref = "ctrl")

# Stratified comparison
results <- compare(data, compare = "treatment", ref = "ctrl", within = "timepoint")

# Pairwise test
results <- compare(data, compare = "treatment", ref = "ctrl",
                   method = "pairwise", test = "wilcoxon")

# Bayes factor test (uses bf_threshold instead of alpha/FDR)
results <- compare(data, compare = "treatment", ref = "ctrl",
                   method = "pairwise", test = "bayes_t", bf_threshold = 10)
} # }