Differential abundance analysis for proteomics data.
pepdiff helps proteomics researchers answer: “What’s differentially abundant?”
- 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
Install from GitHub:
# install.packages("pak")
pak::pak("TeamMacLean/pepdiff")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)- 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
peppwR answers “How many samples do I need?” (power analysis) pepdiff answers “What’s differentially abundant?” (analysis)
See peppwR for experimental design planning.
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
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
Contributions welcome! Please open an issue or submit a pull request.
MIT