RAPTOR v2.2.0 - Ensemble Analysis + Enhanced Optimization

RAPTOR v2.2.0

RNA-seq Analysis Pipeline Testing and Optimization Resource

Highlights

NEW: Module 9 - Ensemble Analysis
Combine DE results from multiple methods (DESeq2, edgeR, limma) for robust consensus gene lists.

• Fisher's Method
• Brown's Method (correlation-aware)
• Robust Rank Aggregation (RRA)
• Voting Consensus
• Weighted Ensemble

EXPANDED: Module 8 - Parameter Optimization
Four optimization approaches (previously just one):

• Ground Truth optimization
• FDR Control optimization
• Stability-based optimization
• Reproducibility-based optimization

ENHANCED: Module 3 - Data Profiler
32-feature profiling with key BCV metric for ML-based pipeline recommendations.

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Documentation

• User Guide - Complete tutorial
• API Docs - Python reference
• Quick Reference - Command cheat sheet
• Migration Guide - Upgrade from v2.1.x

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Breaking Changes

Module 8 renamed and expanded. Pipeline structure reorganized.

See CHANGELOG and Migration Guide for details.

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Installation

PyPI:

pip install raptor-rnaseq==2.2.0

Conda:

# Core environment
conda env create -f environment.yml

# Full environment (includes STAR, Salmon, R packages)
conda env create -f environment-full.yml

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Full Changelog

See CHANGELOG.md for complete details.

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Acknowledgments

Thanks to the bioinformatics community for feedback and suggestions!

Citation: See CITATION.cff
DOI: 10.5281/zenodo.17607161