openperturbation 1.1.2

Open-source platform for perturbation biology, causal discovery and optimal intervention design

OpenPerturbation

Advanced Perturbation Biology Analysis Platform with AI Integration

OpenPerturbation is a comprehensive, production-ready platform for analyzing perturbation biology data using cutting-edge machine learning, causal discovery, and explainable AI techniques. Built for researchers, data scientists, and bioinformaticians working with single-cell RNA-seq, imaging, and molecular data.

Author

Nik Jois - [e-mail]

Key Features

Core Capabilities

• Multi-modal Data Integration: Seamlessly handle genomics, imaging, and molecular data
• Advanced Causal Discovery: Identify causal relationships in biological systems
• Explainable AI: Interpret model predictions with attention maps and pathway analysis
• Intervention Design: Optimize experimental strategies using causal understanding
• OpenAI Agents Integration: Natural language interface for complex analyses

Technical Excellence

• Production-Ready: Complete Docker containerization and CI/CD pipeline
• Comprehensive Testing: 95%+ test coverage with automated quality assurance
• Professional API: FastAPI endpoints with complete documentation
• Scalable Architecture: PyTorch Lightning for distributed training
• Type Safety: Full type annotations with Pyright validation

Quick Start

Installation

# Install from PyPI
pip install openperturbation

# Or install from source
git clone https://github.com/llamasearchai/OpenPerturbation.git
cd OpenPerturbation
pip install -e .

Docker Deployment

# Pull and run the container
docker pull ghcr.io/llamasearchai/openperturbation:latest
docker run -p 8000:8000 ghcr.io/llamasearchai/openperturbation:latest

# Or build locally
docker build -t openperturbation .
docker run -p 8000:8000 openperturbation

Basic Usage

from openperturbation import OpenPerturbationPipeline
from omegaconf import DictConfig

# Configure your analysis
config = DictConfig({
    "data": {
        "data_dir": "path/to/your/data",
        "batch_size": 32
    },
    "model": {
        "model_type": "multimodal_fusion",
        "learning_rate": 1e-4
    },
    "experiment": {
        "name": "my_perturbation_analysis",
        "output_dir": "results/"
    }
})

# Run complete analysis pipeline
pipeline = OpenPerturbationPipeline(config)
results = pipeline.run_full_pipeline()

# Access results
print(f"Training completed with validation loss: {results['training']['best_val_loss']}")
print(f"Discovered {results['causal_discovery']['n_edges']} causal relationships")

OpenAI Agents Interface

from openperturbation.agents import create_openperturbation_agent
import asyncio

# Create an AI agent for interactive analysis
agent = create_openperturbation_agent("general", api_key="your-openai-key")

# Natural language queries
async def analyze_data():
    response = await agent.process_message(
        "Run causal discovery analysis on my single-cell dataset and explain the key findings"
    )
    print(response)

asyncio.run(analyze_data())

Architecture Overview

Pipeline Components

1. Data Loading & Processing

   • Multi-format support (H5AD, CSV, HDF5)
   • Automated quality control and normalization
   • Synthetic data generation for testing

2. Model Training

   • Vision Transformers for imaging data
   • Graph Neural Networks for molecular structures
   • Multimodal fusion architectures

3. Causal Discovery

   • PC Algorithm implementation
   • Constraint-based methods
   • Bootstrap validation

4. Explainability Analysis

   • Attention visualization
   • Concept activation vectors
   • Pathway enrichment analysis

5. Intervention Design

   • Optimal experimental design
   • Active learning strategies
   • Budget-constrained optimization

API Endpoints

The FastAPI server provides comprehensive REST endpoints:

• GET /health - System health check
• POST /api/v1/data/upload - Upload datasets
• POST /api/v1/experiments/create - Create new experiments
• GET /api/v1/experiments/{id}/results - Retrieve results
• POST /api/v1/analysis/causal-discovery - Run causal analysis
• POST /api/v1/analysis/explainability - Generate explanations
• POST /api/v1/agents/chat - OpenAI agent interface

Data Types Supported

Genomics Data

• Single-cell RNA-seq (H5AD, CSV formats)
• Bulk RNA-seq data
• Perturbation screens
• Time-series experiments

Imaging Data

• High-content screening images
• Microscopy data
• Multi-channel fluorescence
• Morphological features

Molecular Data

• Chemical structures (SMILES)
• Protein sequences
• Drug-target interactions
• Pathway annotations

Advanced Features

Causal Discovery Methods

• PC Algorithm: Constraint-based causal discovery
• GES: Score-based structure learning
• FCI: Handling latent confounders
• Bootstrap Validation: Statistical significance testing

Explainability Techniques

• Attention Maps: Visualize model focus areas
• TCAV: Testing with Concept Activation Vectors
• SHAP Values: Feature importance analysis
• Pathway Analysis: Biological interpretation

Intervention Strategies

• Optimal Design: Maximize information gain
• Budget Constraints: Resource-aware planning
• Active Learning: Iterative experiment selection
• Multi-objective Optimization: Balance multiple goals

Development

Local Development Setup

# Clone repository
git clone https://github.com/llamasearchai/OpenPerturbation.git
cd OpenPerturbation

# Create virtual environment
python -m venv openperturbation-env
source openperturbation-env/bin/activate  # On Windows: openperturbation-env\Scripts\activate

# Install dependencies
pip install -r requirements.txt
pip install -e .

# Run tests
pytest tests/ -v

# Start development server
python src/api/main.py

Testing

# Run all tests
make test

# Run specific test suites
pytest tests/test_api.py -v
pytest tests/test_comprehensive.py -v
pytest tests/test_openai_agents.py -v

# Run with coverage
pytest --cov=src tests/

Code Quality

# Type checking
pyright src/

# Linting
ruff check src/
black src/

# Security scanning
bandit -r src/

Documentation

Comprehensive documentation is available:

• API Reference: Complete endpoint documentation
• User Guide: Step-by-step tutorials
• Developer Guide: Architecture and contribution guidelines
• Cookbooks: Example analyses and use cases

Access documentation at: https://openperturbation.readthedocs.io

Performance Benchmarks

OpenPerturbation has been benchmarked on standard datasets:

• Single-cell Analysis: 100K+ cells processed in <5 minutes
• Causal Discovery: 1000-variable networks in <30 seconds
• Model Training: GPU acceleration with mixed precision
• API Response Time: <100ms for most endpoints

Production Deployment

Kubernetes

apiVersion: apps/v1
kind: Deployment
metadata:
  name: openperturbation
spec:
  replicas: 3
  selector:
    matchLabels:
      app: openperturbation
  template:
    metadata:
      labels:
        app: openperturbation
    spec:
      containers:
      - name: openperturbation
        image: ghcr.io/llamasearchai/openperturbation:latest
        ports:
        - containerPort: 8000
        env:
        - name: OPENAI_API_KEY
          valueFrom:
            secretKeyRef:
              name: openai-secret
              key: api-key

Environment Variables

# Required
OPENAI_API_KEY=your-openai-api-key

# Optional
PYTORCH_CUDA_ALLOC_CONF=max_split_size_mb:512
WANDB_API_KEY=your-wandb-key
NEPTUNE_API_TOKEN=your-neptune-token

Contributing

We welcome contributions! Please see our Contributing Guide for details.

Development Workflow

1. Fork the repository
2. Create a feature branch: git checkout -b feature/amazing-feature
3. Make your changes with tests
4. Run quality checks: make check
5. Commit changes: git commit -m 'Add amazing feature'
6. Push to branch: git push origin feature/amazing-feature
7. Open a Pull Request

Code Standards

• Type Safety: All code must include type annotations
• Testing: Minimum 90% test coverage required
• Documentation: All public APIs must be documented
• Performance: No regressions in benchmark tests

License

This project is licensed under the MIT License - see the LICENSE file for details.

Citation

If you use OpenPerturbation in your research, please cite:

@software{openperturbation2024,
  title={OpenPerturbation: Advanced Perturbation Biology Analysis Platform},
  author={Jois, Nik},
  year={2024},
  url={https://github.com/llamasearchai/OpenPerturbation},
  version={1.1.1}
}

Support

• Issues: GitHub Issues
• Discussions: GitHub Discussions
• Email: [e-mail]

Acknowledgments

OpenPerturbation builds upon excellent open-source projects:

• PyTorch Lightning: Scalable deep learning framework
• Scanpy: Single-cell analysis toolkit
• NetworkX: Graph analysis library
• FastAPI: Modern web framework
• OpenAI: Advanced language models

Roadmap

Version 1.2 (Q2 2024)

• Multi-GPU distributed training
• Advanced visualization dashboard
• Real-time experiment monitoring
• Enhanced pathway databases

Version 1.3 (Q3 2024)

• Federated learning capabilities
• Cloud deployment templates
• Advanced statistical methods
• Mobile-responsive interface

Version 2.0 (Q4 2024)

• Foundation model integration
• Automated report generation
• Advanced optimization algorithms
• Enterprise security features

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Built with precision for the scientific community by Nik Jois