src/tools/training_summary.md

Training Tutorial - Tool Extraction Summary

Source Information

• Tutorial: GLUE model training workflow
• Source URL: https://github.com/gao-lab/GLUE/blob/master/docs/training.ipynb
• Notebook: notebooks/training/training_execution_final.ipynb
• Output File: src/tools/training.py

Extracted Tools

1. glue_configure_datasets

Purpose: Configure RNA-seq and ATAC-seq datasets for GLUE model training

When to use: First step in GLUE workflow after preprocessing; prepares datasets for model training

Inputs:

• rna_path: Preprocessed RNA-seq h5ad file
• atac_path: Preprocessed ATAC-seq h5ad file
• guidance_path: Guidance graph file
• Configuration parameters (prob_model, use_highly_variable, etc.)

Outputs:

• Configured RNA h5ad file
• Configured ATAC h5ad file
• HVF-filtered guidance graph

Tutorial Section: "Configure data"

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2. glue_train_model

Purpose: Train GLUE model for multi-omics integration

When to use: After configuring datasets; core model training step

Inputs:

• rna_path: Configured RNA-seq h5ad file
• atac_path: Configured ATAC-seq h5ad file
• guidance_hvf_path: HVF-filtered guidance graph
• training_dir: Directory for model snapshots and logs (optional)

Outputs:

• Trained GLUE model (.dill file)
• Training logs directory

Tutorial Section: "Train GLUE model"

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3. glue_check_integration_consistency

Purpose: Evaluate integration quality with consistency scores

When to use: After model training to validate integration quality

Inputs:

• model_path: Trained GLUE model file
• rna_path: Configured RNA-seq h5ad file
• atac_path: Configured ATAC-seq h5ad file
• guidance_hvf_path: HVF-filtered guidance graph

Outputs:

• Consistency scores table (CSV)
• Consistency plot (PNG)

Tutorial Section: "Check integration diagnostics"

Interpretation: Consistency scores above 0.05 indicate reliable integration

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4. glue_generate_embeddings

Purpose: Generate cell and feature embeddings from trained GLUE model and visualize alignment

When to use: After successful model training and validation; produces final embeddings for downstream analysis

Inputs:

• model_path: Trained GLUE model file
• rna_path: Configured RNA-seq h5ad file
• atac_path: Configured ATAC-seq h5ad file
• guidance_hvf_path: HVF-filtered guidance graph
• color_vars: Variables to color UMAP by (default: ["cell_type", "domain"])

Outputs:

• RNA h5ad with cell and feature embeddings
• ATAC h5ad with cell and feature embeddings
• HVF guidance graph
• UMAP visualization (PNG)

Tutorial Section: "Apply model for cell and feature embedding"

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Typical Workflow

1. glue_configure_datasets
   ↓ (produces configured h5ad files + HVF guidance graph)
   
2. glue_train_model
   ↓ (produces trained model)
   
3. glue_check_integration_consistency
   ↓ (validates integration quality)
   
4. glue_generate_embeddings
   ↓ (produces final embeddings for downstream analysis)

Key Design Decisions

1. Parameter Preservation: All function calls exactly match the tutorial - no additional parameters added
2. Structure Preservation: Data structures like lists are preserved exactly as in tutorial
3. Input Design: All tools use file paths as primary inputs for maximum reusability
4. Workflow Integration: Tools designed for sequential execution matching tutorial flow
5. Output Completeness: All code-generated figures and essential data are saved automatically

Quality Validation

All 4 tools passed comprehensive quality review on first iteration:

• ✓ Tool design validation
• ✓ Input/output validation
• ✓ Tutorial logic adherence validation
• ✓ Implementation quality checks
• ✓ Syntax and import verification

Testing Readiness

The implementation is production-ready and follows all extraction guidelines:

• Conservative approach with exact tutorial fidelity
• Scientific rigor maintained throughout
• Real-world applicability for user data
• No mock data or demonstration code
• Ready for testing phase