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Perform differential expression analysis on proteomics data

Source: R/DEAnalysis.R
de_analysis_proteomics.Rd

Coordinates comparison specification, limma analysis, and result extraction.

Usage

de_analysis_proteomics(
  se,
  assay_name = NULL,
  comparisons = NULL,
  control = NULL,
  logFC_threshold = 0,
  alpha = 0.05,
  p_adj = TRUE,
  eBayes_trend = NULL,
  eBayes_robust = NULL,
  de_method = "limma",
  covariate_column = NULL,
  bio_replicate_column = NULL,
  condition_column = "Condition",
  verbose = TRUE
)

Arguments

se

SummarizedExperiment with imputed assay (output of impute_proteomics)

assay_name

Assay name to use. If NULL, uses the last available assay

comparisons

Comparisons to perform. If NULL, auto-generates (pairwise or vs control)

control

Control condition. If NULL, generates all pairwise comparisons

logFC_threshold

LogFC threshold for significance (default: 0)

alpha

Adjusted p-value threshold (default: 0.05)

p_adj

Use adjusted p-value (default: TRUE)

eBayes_trend

Use trend estimation in eBayes (default: TRUE, recommended for proteomics)

eBayes_robust

Use robust estimation in eBayes (default: TRUE, recommended for proteomics)

de_method

DE method: "limma" (default) or "limpa" (probabilistic, requires imp_method="limpa")

covariate_column

Column name(s) in colData for paired/blocked design. Single string (e.g., "Subject") or character vector (e.g., c("Subject", "Batch")). Default: NULL

bio_replicate_column

Column name in colData identifying biological replicates (e.g., "Patient", "Subject"). Used with limma::duplicateCorrelation() to account for technical replicates or paired designs via random effect blocking. Default: NULL

condition_column

Condition column name (default: "Condition")

verbose

Print progress messages (default: TRUE)

Value

List with:

  • DEPs_results: Data frame with differential expression results

  • comparisons: Comparisons performed

  • assay_name: Assay name used

Examples

data(nadia_dia)
norm <- normalize_proteomics(nadia_dia, norm_method = "cycloess",
                             verbose = FALSE)
imp  <- impute_proteomics(norm$se, normalized_assay_name = "cycloess",
                          verbose = FALSE)

# All pairwise comparisons
de <- de_analysis_proteomics(imp$se, assay_name = "Impseqrob_min",
                             verbose = FALSE)
head(de$DEPs_results[, c("Protein.IDs", "Comparison", "logFC", "adj.P.Val")])
#>         Protein.IDs Comparison       logFC    adj.P.Val
#> 1        A0A024RBG1        B-A  0.08293965 7.389228e-01
#> 2        A0A024RBG1        D-A -0.01391145 9.511693e-01
#> 3        A0A024RBG1        D-B -0.09685110 7.151013e-01
#> 4 A0A140T897;P02769        B-A  0.13439560 3.118509e-03
#> 5 A0A140T897;P02769        D-B  0.20481573 7.760613e-05
#> 6 A0A140T897;P02769        D-A  0.33921133 5.778484e-07

# Or every condition against a single control
de2 <- de_analysis_proteomics(imp$se, assay_name = "Impseqrob_min",
                              control = "A", verbose = FALSE)
unique(de2$DEPs_results$Comparison)
#> [1] "B-A" "D-A"