bioscience-engine 0.2.0
Pure Python computational microscopy engine with OpenCV
Requires Python: >=3.8

# BioScience Engine - Computational Microscopy Analysis

[![Python](https://img.shields.io/badge/Python-3.8+-blue.svg)](https://python.org)
[![OpenCV](https://img.shields.io/badge/OpenCV-4.5+-green.svg)](https://opencv.org)
[![License](https://img.shields.io/badge/License-MIT-green.svg)](LICENSE)

A comprehensive computational microscopy platform for automated cell tracking and analysis across diverse biological research domains. Built on Python with OpenCV, NumPy, SciPy, and Pandas for seamless integration into scientific workflows.

## Key Features

- Automated cell detection and tracking across time-lapse sequences
- Multi-channel fluorescence analysis for protein expression and localization
- Cell division and apoptosis detection for proliferation studies
- Advanced morphology analysis quantifying shape and structural features
- Interactive visualization tools for result validation and exploration
- Batch processing capabilities for high-throughput screening

## Primary Applications

### Cancer Research
Track tumor cell migration, metastasis patterns, drug response testing, and quantify cell division rates under different treatment conditions.

### Drug Discovery & Development
Automate high-throughput screening, generate dose-response curves, assess toxicity, and measure time-to-effect for compound evaluation.

### Stem Cell Research
Monitor differentiation processes, track colony formation patterns, trace cell lineages, and ensure quality control in biomanufacturing.

### Immunology Studies
Analyze T-cell migration, study cell-cell interactions, monitor inflammatory responses, and evaluate vaccine efficacy.

### Wound Healing & Regeneration
Measure cell migration speeds, study collective cell movement patterns, and test tissue repair strategies.

### Neuroscience
Track neuron development, monitor synaptic plasticity, study neurodegeneration progression, and test neuroprotective compounds.

### Microbiology
Analyze bacterial growth dynamics, study antibiotic resistance, track cell division patterns, and monitor biofilm formation.

## Installation

```bash
git clone https://github.com/yourusername/bioscience-engine
cd bioscience-engine
pip install -e .
```

### Dependencies
- OpenCV 4.5+
- NumPy 1.21+
- SciPy 1.7+
- Pandas 1.3+
- Matplotlib 3.4+

## Usage Example

```python
import bioscience_engine as bio

# Load and analyze time-lapse data
pipeline = bio.Pipeline()
pipeline.load_images("experiment_01/")
pipeline.set_parameters(
    min_cell_size=100,
    detection_threshold=0.8
)

# Execute complete analysis workflow
pipeline.denoise()
pipeline.detect_cells()
trajectories = pipeline.track_cells()

# Access comprehensive results
print(f"Tracked {len(trajectories)} cells across {trajectories.time_points} frames")
print(f"Detected {trajectories.division_events} cell divisions")
print(f"Identified {trajectories.apoptosis_events} cell death events")

# Export for further analysis
trajectories.export_csv("cell_analysis_results.csv")
trajectories.generate_report("experiment_summary.pdf")
```

## Analysis Pipeline

The platform provides a complete workflow for computational microscopy:

```
Image Loading -> Preprocessing -> Cell Detection -> Tracking -> Feature Extraction -> Visualization
       |              |              |            |             |                  |
   Multiple      Denoising      Segmentation  Trajectory    Morphology      Interactive
   Formats       Registration   Multi-scale   Linking       Statistics      Plots & Videos
```

### Core Analysis Modules

- Cell Detection: Multi-scale segmentation adapting to varying cell sizes and densities
- Tracking Algorithm: Robust trajectory linking handling cell divisions and apoptosis
- Morphology Analysis: Quantitative shape descriptors including area, circularity, eccentricity
- Multi-channel Analysis: Fluorescence quantification and colocalization studies
- Statistical Analysis: Built-in hypothesis testing and effect size calculations

## Advanced Capabilities

### Cell Division Detection
Automatically identify mitosis events and establish parent-daughter relationships with precise timing of division events.

### Apoptosis Detection
Monitor cell death through shrinkage, membrane blebbing, intensity changes, and fragmentation patterns.

### Multi-Channel Support
Analyze fluorescence across brightfield, GFP, RFP, DAPI and other channels for comprehensive protein expression studies.

### Interactive Correction Tools
User-friendly interface for manual track editing, cell addition/deletion, and result validation.

## Output & Export

- Trajectory Data: Complete cell tracks with speed, direction, and persistence metrics
- Event Logs: Division and apoptosis events with timing and confidence scores
- Morphology Reports: Quantitative shape analysis for each cell over time
- Statistical Summaries: Condition comparisons with p-values and effect sizes
- Visualization: Track overlays, migration plots, and event annotations
- Multiple Formats: CSV, Excel, HDF5 for integration with other analysis tools

## Designed for Researchers

### Biologist-Friendly
- Intuitive Python API requiring minimal programming experience
- Interactive GUI for manual correction and exploration
- Comprehensive documentation with example datasets
- Pre-configured analysis pipelines for common experiments

### Computational Flexibility
- Modular architecture for custom analysis workflows
- Easy integration with existing Python scientific stacks
- Extensible framework for adding new algorithms
- Compatibility with common microscopy data formats

## Contributing

We welcome contributions from the scientific community. Please see our contributing guidelines for:
- Code standards following PEP 8
- Testing requirements for new features
- Documentation updates
- Example datasets and use cases

## License

MIT License - see LICENSE file for complete details.

## Authors

- Elijah Manda - Initial implementation and core architecture

## Acknowledgments

This project builds upon open-source scientific Python libraries including OpenCV, NumPy, SciPy, Pandas, and the broader scientific Python ecosystem.

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Empowering biological discovery through computational microscopy