TimeFlies

TimeFlies Development Roadmap

Transcriptomics aging research computational tool - Updated August 2024

🎉 Major Achievements (Completed)

✅ Step 1: Automated Multi-Model Training System

Status: Complete with comprehensive implementation

• ModelQueueManager: Sequential training of multiple model configurations
• CLI Integration: timeflies queue command with extensive options
• GUI Integration: “Run Model Queue” tab in TimeFlies launcher
• Features: Checkpoint/resume, configuration overrides, progress tracking
• Testing: 12 comprehensive tests, Python 3.12 compatible
• Documentation: Complete user guide with examples

✅ Step 2: Hyperparameter Tuning System

Status: Complete with full integration

• HyperparameterTuner: Grid, random, and Bayesian optimization (Optuna)
• CLI Integration: timeflies tune command with resume capability
• GUI Integration: Complete “Hyperparameter Tuning” tab
• Features: Configurable metrics, CNN variants, search optimizations
• Architecture: Project-specific outputs, model queue integration
• Testing: Unit and integration tests with proper mocking
• Documentation: Comprehensive guide with optimization metrics

✅ Step 3: Batch Correction with Automatic Environment Management

Status: Complete with comprehensive implementation

• BatchCorrector: scVI integration with proper train/eval workflow
• Environment Management: Seamless switching between main and batch environments
• Per-Project Control: Enable/disable batch correction via configuration
• CLI Integration: timeflies batch-correct with automatic dependency management
• ML Best Practices: Prevents data leakage using proper train/query workflow
• Testing: Real dependency testing with automatic environment switching
• Documentation: Updated installation and workflow documentation

✅ Infrastructure & Quality

• Package Management: Modern pyproject.toml with proper dependencies
• Code Quality: Pre-commit hooks, ruff linting, proper type hints
• Testing Framework: pytest with coverage reporting, test markers
• Environment Management: Dual environment system (main + batch correction)
• Documentation: Comprehensive README with output structure
• Git Workflow: Feature branches, proper commit messages, CI/CD ready

✅ Recent User Experience Improvements (December 2024)

• Config Update Fix: Resolved issue where setup.yaml showed as different on every update
• Test Infrastructure: Fixed mock return value inconsistencies in integration tests
• Documentation: Updated all docs to reflect modular configs/ directory structure
• Split Data Utility: Created extract_clean_splits.py for copying batch-corrected to non-batch files
• Setup Process: Enhanced –force-split functionality with proper validation

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🔬 Research & Analysis Tasks

High Priority: Research Deliverables

• Alzheimer’s Dataset Analysis
  • Execute batch correction on Alzheimer’s datasets
  • Run comprehensive model training with hyperparameter tuning
  • Generate publication-ready results and visualizations
  • Deliver analysis results to collaborators
• Performance Benchmarking
  • Compare CNN vs traditional ML approaches across datasets
  • Document optimal hyperparameters for different tissue types
  • Validate model generalizability across aging conditions

Analysis Improvements

• Enhanced SHAP Integration
  • Expand SHAP analysis beyond basic feature importance
  • Add pathway-level interpretability analysis
  • Integrate with existing biological databases
• Statistical Analysis
  • Add statistical significance testing for model comparisons
  • Implement cross-validation analysis for model stability
  • Generate confidence intervals for predictions

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🚀 Technical Enhancements

Testing & Quality (Current: ~40% coverage)

• Expand Test Coverage
  • Target: 80%+ coverage across all modules
• Code Quality Improvements
  • Review and update all docstrings for accuracy
  • Ensure consistent error handling patterns
  • Optimize memory usage for large datasets
  • Performance profiling and optimization
  • Improve testing naming
  • Implement config-based logging system - Currently logging uses hardcoded values in logging_config.py. The logging section in default.yaml is planned but not yet integrated with the actual logging system

Advanced Features

• Multi-GPU Support
  • Implement distributed training for large models
  • Add GPU memory management for hyperparameter tuning
  • Scale model queue system for HPC environments
• Data Pipeline Enhancements
  • Add support for additional single-cell formats
  • Implement streaming data processing for very large datasets
  • Add data quality validation and reporting
• Batch Correction Storage Optimization
  • Current: Maintain separate batch-corrected and non-batch files (e.g., *_train.h5ad + *_train_batch.h5ad)
  • Proposed: Store original data within batch-corrected files and extract when batch correction not needed
  • Benefits: Reduce storage duplication, simplify data management, single source of truth
  • Implementation: Investigate AnnData layers/obsm storage for original expression data
  • Impact: Could halve storage requirements and eliminate file sync issues
• Visualization Improvements
  • Interactive plots with Plotly integration
  • Enhanced EDA reporting with automated insights
  • Real-time training progress visualization

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📦 Deployment & Distribution

Package Management

• PyPI Deployment
  • Finalize package metadata and dependencies
  • Test installation from PyPI test repository
  • Create comprehensive installation documentation
  • Set up automated releases via GitHub Actions
• Containerization
  • Create optimized Docker images for different use cases
  • Add Singularity support for HPC environments
  • Document container usage patterns

Documentation & Tutorials

• User Documentation
  • Create step-by-step tutorials for common research workflows
  • Add troubleshooting guide for common issues
  • API documentation with Sphinx
• Developer Documentation
  • Architecture overview and design decisions
  • Contributing guidelines and code standards
  • Testing procedures and CI/CD setup

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💡 Future Research Directions

Advanced Modeling

• Deep Learning Architectures
  • Investigate transformer-based models for sequence analysis
  • Explore graph neural networks for cell-cell interactions
  • Multi-modal learning combining expression and metadata
• Biological Integration
  • Pathway-aware model architectures
  • Integration with protein-protein interaction networks
  • Temporal modeling for longitudinal aging studies

Platform Extensions

• Multi-Species Support
  • Extend beyond Drosophila to mouse and human datasets
  • Cross-species transfer learning capabilities
  • Comparative aging analysis tools
• Clinical Integration
  • Biomarker discovery pipelines
  • Clinical prediction models
  • Integration with medical imaging data

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📋 Development Notes

Current Architecture Status

• ✅ Modular Design: Well-structured with clear separation of concerns
• ✅ Configuration System: Flexible YAML-based configuration
• ✅ CLI/GUI Parity: Full functionality available in both interfaces
• ✅ Testing Framework: Comprehensive test suite with proper mocking
• ✅ Documentation: Complete user and developer guides

Technology Stack

• Core: Python 3.12+, TensorFlow/Keras, scikit-learn
• Optimization: Optuna for Bayesian hyperparameter tuning
• Data: AnnData, pandas, NumPy for single-cell data handling
• Visualization: matplotlib, seaborn for analysis plots
• GUI: tkinter for cross-platform desktop interface
• Testing: pytest, coverage.py for comprehensive testing

Development Priorities

1. User Experience & Stability - Address real-world usage issues and improve workflows
2. Research Deliverables - Complete analysis for ongoing publications
3. Data Management Optimization - Investigate batch correction storage improvements
4. Testing Expansion - Achieve >80% coverage for production readiness
5. Performance Optimization - Scale to larger datasets and HPC environments
6. PyPI Deployment - Enable easy installation for the research community

Quality Standards

• All new features must include comprehensive tests
• Documentation must be updated with every feature addition
• Code must pass all linting and type checking
• Breaking changes require migration guides
• Performance regressions are not acceptable
• Remove Emojis unless needed.

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Last Updated: December 2024 Next Review: After research publication milestones

Recent Development Focus

The current development cycle has focused on user experience improvements and stability fixes based on real-world usage feedback. Key areas include:

• Fixing update command behavior and config management
• Improving documentation accuracy and clarity
• Enhancing data management utilities and workflows
• Strengthening test infrastructure and CI/CD reliability

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