Power Analysis for Phosphopeptide Abundance Hypothesis Test

Estimate best fit distributions and do power analysis for hypothesis tests on phosphopeptide abundance data.

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

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