Organoid Data Analysis Framework

A modular analytical framework addressing reproducibility, heterogeneity, fidelity, integration, and predictive modeling in organoid systems.
Organoid models present analytical challenges that extend beyond standard single-omics workflows.
This repository consolidates five research-grade notebooks, each designed as an executable, interpretable analysis that combines code, rationale, and results in a single, transparent workflow.

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Analytical focus

• Reproducibility — addresses technical variation and batch effects through structured QC and correction.
• Heterogeneity — quantifies intra- and inter-organoid variability in cell composition and state.
• Fidelity — benchmarks organoid profiles against fetal and adult tissue references to measure biological accuracy.
• Integration — combines RNA and protein modalities to uncover shared and unique molecular programs.
• Prediction — applies interpretable machine-learning models to predict organoid quality and identify molecular drivers.

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Repository structure

The framework includes five core modules, each with its own notebook and README:

1. Quality Control & Batch Correction — nb1_QC.ipynb
2. Organoid Heterogeneity — nb2_heterogeneity.ipynb
3. Organoid Fidelity vs Reference — nb3_reference.ipynb
4. Multi-Omic Integration — nb4_multiomics.ipynb
5. Predictive Modeling of Organoid Quality — nb5_quality_ml.ipynb

Each notebook integrates code, commentary, and figures, forming a reproducible pipeline from data QC → heterogeneity → fidelity → integration → predictive modeling.

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Key features

• Transparent analyses: every step documented with rationale and inline results.
• Multi-omic integration: RNA–protein factorization linking expression and proteomic signals.
• Interpretable ML: model features mapped back to biological factors and pathways.
• Reproducible workflow: consistent directory structure, deterministic setup, and complete outputs within notebooks.
• Modular design: individual modules stand alone but form a coherent end-to-end framework.

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Data availability

Raw datasets and large intermediates are excluded due to size constraints.
Each module’s README specifies dataset sources and assumptions.
All notebooks contain embedded results and figures for review.

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Citation

Yepes, S. (2025). Organoid Data Analysis Framework: Reproducibility, Heterogeneity, and Fidelity in Organoid Systems. GitHub.
DOI: 10.5281/zenodo.17268384

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📧 sallyepes233@gmail.com