Description
Differential Abundance Analysis for Phosphoproteomics Data.
Description
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) <doi:10.2307/2344614>), Aligned Rank Transform (ART; Wobbrock et al. (2011) <doi:10.1145/1978942.1978963>), and pairwise test methods for statistical analysis. Includes visualization functions for Principal Component Analysis (PCA), volcano plots, and heatmaps.
README.md
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.