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pepdiff

Differential abundance analysis for proteomics data.

pepdiff helps proteomics researchers answer: “What’s differentially abundant?”

Features

  • GLM analysis – Gamma GLM with emmeans for factorial designs
  • ART analysis – Non-parametric alternative for heavy-tailed data
  • Pairwise tests – Wilcoxon, bootstrap-t, Bayes factor, rank products
  • Stratified comparisons – Analyse effects within factor levels
  • Fit diagnostics – Visual checks for GLM model assumptions
  • Rich visualizations – Volcano plots, heatmaps, PCA, p-value histograms

Installation

Install from GitHub:

# install.packages("pak")
pak::pak("TeamMacLean/pepdiff")

Quick Start 

library(pepdiff)

# Import data
dat <- read_pepdiff(
  "data.csv",
  id = "peptide",
  gene = "gene_id",
  value = "abundance",
  factors = c("treatment", "timepoint"),
  replicate = "bio_rep"
)

# Run differential analysis
results <- compare(
  dat,
  compare = "treatment",
  ref = "ctrl",
  method = "glm"
)

# Visualize results
plot(results)

Documentation

Companion Package

peppwR answers “How many samples do I need?” (power analysis) pepdiff answers “What’s differentially abundant?” (analysis)

See peppwR for experimental design planning.

Workflow 

flowchart LR
    A[CSV] --> B[read_pepdiff]
    B --> C[pepdiff_data]
    C --> D[compare]
    D --> E[pepdiff_results]
    E --> F[plot]

    style A fill:#FFFFCC,stroke:#BD0026
    style B fill:#FD8D3C,stroke:#BD0026,color:#fff
    style C fill:#FFFFCC,stroke:#BD0026
    style D fill:#FD8D3C,stroke:#BD0026,color:#fff
    style E fill:#FFFFCC,stroke:#BD0026
    style F fill:#FD8D3C,stroke:#BD0026,color:#fff

Citation

If you use pepdiff in your research, please cite:

MacLean, D. (2026). pepdiff: Differential Abundance Analysis for
Proteomics Data. R package version 1.0.0.
https://github.com/TeamMacLean/pepdiff

Contributing

Contributions welcome! Please open an issue or submit a pull request.

License

MIT