peppwR

Power analysis for phosphopeptide abundance hypothesis tests via simulation.

peppwR helps proteomics researchers answer critical experimental design questions:

• “What sample size do I need?” — Find the N required for 80% power to detect a given effect
• “What’s my power?” — Calculate power for a given sample size and effect
• “What can I detect?” — Find the minimum detectable effect size for your design

Features

• Distribution fitting — Fit gamma, lognormal, normal, and other distributions to pilot data
• Per-peptide analysis — Get power estimates across your entire peptidome
• Multiple statistical tests — Wilcoxon, bootstrap-t, and Bayes factor t-tests
• Missing data handling — Model MNAR (Missing Not At Random) patterns common in proteomics
• FDR-aware mode — Account for multiple testing correction in power calculations
• Rich visualizations — Power curves, heatmaps, QQ plots, and diagnostic plots

Installation

Install the development version from GitHub:

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

Quick Start

From Pilot Data (Per-Peptide Mode)

library(peppwR)

# Fit distributions to your pilot data
fits <- fit_distributions(
  pilot_data,
  id = "peptide",
  group = "condition",
  value = "abundance"
)

# Find required sample size for 80% power to detect 2-fold change
result <- power_analysis(
  fits,
  effect_size = 2,
  target_power = 0.8,
  find = "sample_size"
)

print(result)
#> Per-peptide power analysis
#> Question: sample_size
#> Answer: N = 6 per group
#> 73% of peptides achieve 80% power

plot(result)

From Scratch (Aggregate Mode)

No pilot data? Specify assumed distribution parameters:

result <- power_analysis(
  distribution = "gamma",
  params = list(shape = 2, rate = 0.1),
  effect_size = 2,
  target_power = 0.8,
  find = "sample_size"
)

Documentation

• Getting Started — Introduction and basic usage
• Power Analysis Workflow — Complete workflow with real data
• Benchmarking — Performance characteristics
• Function Reference — Full API documentation

Real-World Examples

• DDA Time Course Analysis
• PRM Genotype Analysis

Workflow Overview

flowchart TD
    A[pilot_data] --> B[fit_distributions]
    B --> C[power_analysis]
    C --> D{find = ?}
    D --> E["power"]
    D --> F["sample_size"]
    D --> G["effect_size"]
    E --> H[results + plots]
    F --> H
    G --> H

    style A fill:#FFFFCC,stroke:#BD0026
    style B fill:#FD8D3C,stroke:#BD0026,color:#fff
    style C fill:#FD8D3C,stroke:#BD0026,color:#fff
    style H fill:#FFFFCC,stroke:#BD0026

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Citation

If you use peppwR in your research, please cite:

MacLean, D. (2026). peppwR: Power Analysis for Phosphopeptide Abundance
Hypothesis Tests. R package version 0.1.0.
https://github.com/TeamMacLean/peppwR

Contributing

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

License

MIT © Dan MacLean