Differential Abundance Analysis for Phosphoproteomics Data

Provides tools for analyzing differential abundance in proteomics experiments. Implements S3 classes for data management and supports Generalized Linear Models (GLM; Nelder and Wedderburn (1972) ), Aligned Rank Transform (ART; Wobbrock et al. (2011) ), and pairwise test methods for statistical analysis. Includes visualization functions for Principal Component Analysis (PCA), volcano plots, and heatmaps.

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

• Getting Started – Basic workflow
• GLM Analysis – Factorial designs with GLM
• ART Analysis – Non-parametric alternative
• Checking Model Fit – Diagnostic plots
• Pairwise Tests – Direct two-group comparisons
• Function Reference – Full API

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