| Name | DoOR-python-toolkit JSON |
| Version |
0.1.0
JSON |
| download |
| home_page | None |
| Summary | Python toolkit for working with the DoOR (Database of Odorant Responses) database |
| upload_time | 2025-11-06 01:38:23 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | >=3.8 |
| license | MIT License
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| keywords |
bioinformatics
door
drosophila
neuroscience
odorant
olfaction
receptor
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
pyreadr
pandas
numpy
pyarrow
|
| Travis-CI |
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| coveralls test coverage |
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# DoOR Python Toolkit
[](https://badge.fury.io/py/door-python-toolkit)
[](https://opensource.org/licenses/MIT)
[](https://www.python.org/downloads/)
**Python toolkit for working with the DoOR (Database of Odorant Responses) database.**
Extract, analyze, and integrate *Drosophila* odorant-receptor response data in pure Python. No R installation required.
---
## Features
- ✅ **Pure Python** - Extract DoOR R data files without installing R
- 🚀 **Fast** - Parquet-based caching for quick loading
- 🧠 **ML-Ready** - PyTorch/NumPy integration for neural network training
- 📊 **693 odorants × 78 receptors** - Comprehensive *Drosophila* olfactory data
- 🔍 **Search & Filter** - Query by odorant name, receptor, or chemical properties
- 📦 **PyPI Package** - `pip install door-python-toolkit`
---
## Quick Start
### Installation
```bash
pip install door-python-toolkit
```
For PyTorch support:
```bash
pip install door-python-toolkit[torch]
```
### Extract DoOR Data
```python
from door_toolkit import DoORExtractor
# Extract R data files to Python formats
extractor = DoORExtractor(
input_dir="path/to/DoOR.data/data", # Unzipped DoOR R package
output_dir="door_cache"
)
extractor.run()
```
### Use in Your Code
```python
from door_toolkit import DoOREncoder
# Load encoder
encoder = DoOREncoder("door_cache")
# Encode single odorant → 78-dim PN activation vector
pn_activation = encoder.encode("acetic acid")
print(pn_activation.shape) # (78,)
# Encode batch
odors = ["acetic acid", "1-pentanol", "ethyl acetate"]
pn_batch = encoder.batch_encode(odors)
print(pn_batch.shape) # (3, 78)
# Search odorants
acetates = encoder.list_available_odorants(pattern="acetate")
print(f"Found {len(acetates)} acetates") # 36
# Get metadata
stats = encoder.get_receptor_coverage("acetic acid")
print(f"Active receptors: {stats['n_active']}")
```
---
## What is DoOR?
The **Database of Odorant Responses (DoOR)** is a comprehensive collection of odorant-receptor response measurements for *Drosophila melanogaster* (fruit fly).
**Published:** Münch & Galizia (2016), *Scientific Data* 3:160122
**Citation:** https://doi.org/10.1038/sdata.2016.122
**Original R package:** https://github.com/ropensci/DoOR.data
### Dataset Overview
| Metric | Value |
|--------|-------|
| Odorants | 693 compounds |
| Receptors | 78 ORN types (Or, Ir, Gr) |
| Measurements | 7,381 odorant-receptor pairs |
| Sparsity | 86% (typical for chemical screens) |
| Response Range | [0, 1] normalized |
**Top receptors by coverage:**
- Or19a: 71.7% (497/693 odorants)
- Or10a: 33.9% (235/693 odorants)
- Or22a: 32.5% (225/693 odorants)
---
## Use Cases
### 1. **Neuroscience Research**
Map odorants to glomerular activation patterns for modeling olfactory processing.
```python
from door_toolkit import DoOREncoder
import matplotlib.pyplot as plt
encoder = DoOREncoder("door_cache")
# Compare response profiles
odors = ["acetic acid", "ethyl acetate", "1-pentanol"]
responses = [encoder.encode(o) for o in odors]
plt.figure(figsize=(10, 4))
for i, (odor, resp) in enumerate(zip(odors, responses)):
plt.subplot(1, 3, i+1)
plt.bar(range(len(resp)), resp)
plt.title(odor)
plt.xlabel("Receptor")
plt.ylabel("Response")
plt.tight_layout()
plt.show()
```
### 2. **Machine Learning**
Train neural networks with empirical odorant-receptor data.
```python
from door_toolkit import DoOREncoder
import torch
import torch.nn as nn
# Load encoder
encoder = DoOREncoder("door_cache", use_torch=True)
# Create dataset
class OdorDataset(torch.utils.data.Dataset):
def __init__(self, odor_names, labels):
self.encoder = DoOREncoder("door_cache")
self.odor_names = odor_names
self.labels = labels
def __len__(self):
return len(self.odor_names)
def __getitem__(self, idx):
pn_activation = self.encoder.encode(self.odor_names[idx])
return pn_activation, self.labels[idx]
# Train model
model = nn.Sequential(
nn.Linear(78, 256), # 78 = n_receptors
nn.ReLU(),
nn.Linear(256, 2) # Binary classification
)
# ... standard PyTorch training loop
```
### 3. **Chemical Similarity**
Find odorants with similar receptor response patterns.
```python
from door_toolkit.utils import find_similar_odorants
similar = find_similar_odorants(
target_odor="acetic acid",
cache_path="door_cache",
top_k=5,
method="correlation"
)
for name, similarity in similar:
print(f"{name}: {similarity:.3f}")
```
### 4. **Data Analysis**
Export subsets for custom analyses.
```python
from door_toolkit.utils import export_subset, list_odorants
# Export all acetates
acetates = list_odorants("door_cache", pattern="acetate")
export_subset(
cache_path="door_cache",
output_path="acetates.csv",
odorants=acetates
)
```
---
## Command-Line Interface
```bash
# Extract DoOR data
door-extract --input DoOR.data/data --output door_cache
# Validate cache
python -c "from door_toolkit.utils import validate_cache; validate_cache('door_cache')"
# List odorants
python -c "from door_toolkit.utils import list_odorants; print(list_odorants('door_cache', 'acetate'))"
```
---
## API Reference
### `DoORExtractor`
Extract DoOR R data files to Python formats.
**Methods:**
- `run()` - Execute full extraction pipeline
- `extract_response_matrix()` - Extract odorant-receptor matrix
- `extract_odor_metadata()` - Extract chemical properties
### `DoOREncoder`
Encode odorant names to neural activation patterns.
**Methods:**
- `encode(odor_name)` - Encode single odorant
- `batch_encode(odor_names)` - Encode multiple odorants
- `list_available_odorants(pattern)` - Search odorants
- `get_receptor_coverage(odor_name)` - Get coverage stats
- `get_odor_metadata(odor_name)` - Get chemical metadata
### Utilities
Helper functions in `door_toolkit.utils`:
- `load_response_matrix(cache_path)` - Load response data
- `load_odor_metadata(cache_path)` - Load metadata
- `list_odorants(cache_path, pattern)` - List odorants
- `get_receptor_info(cache_path)` - Receptor statistics
- `find_similar_odorants(target, cache_path, top_k)` - Similarity search
- `export_subset(cache_path, output_path, odorants, receptors)` - Export data
- `validate_cache(cache_path)` - Validate cache integrity
---
## Installation from Source
```bash
# Clone repository
git clone https://github.com/yourusername/door-python-toolkit.git
cd door-python-toolkit
# Install in development mode
pip install -e .[dev]
# Run tests
pytest tests/
# Format code
black door_toolkit/
```
---
## Requirements
**Core:**
- Python ≥ 3.8
- pyreadr ≥ 0.4.7
- pandas ≥ 1.5.0
- numpy ≥ 1.21.0
- pyarrow ≥ 12.0.0
**Optional:**
- torch ≥ 2.0.0 (for PyTorch integration)
---
## Data Sources
This toolkit extracts data from the original DoOR R packages:
- **DoOR.data** - https://github.com/ropensci/DoOR.data
- **DoOR.functions** - https://github.com/ropensci/DoOR.functions
To use this toolkit, download and unzip the DoOR R packages:
```bash
# Download from GitHub
wget https://github.com/ropensci/DoOR.data/archive/refs/tags/v2.0.0.zip
unzip v2.0.0.zip
# Extract to Python
python -c "from door_toolkit import DoORExtractor; DoORExtractor('DoOR.data-2.0.0/data', 'door_cache').run()"
```
---
## Contributing
Contributions welcome! Please:
1. Fork the repository
2. Create a feature branch (`git checkout -b feature/amazing-feature`)
3. Commit changes (`git commit -m 'Add amazing feature'`)
4. Push to branch (`git push origin feature/amazing-feature`)
5. Open a Pull Request
**Development setup:**
```bash
git clone https://github.com/yourusername/door-python-toolkit.git
cd door-python-toolkit
pip install -e .[dev]
pytest tests/
```
---
## Citation
If you use this toolkit in your research, please cite both:
**This toolkit:**
```bibtex
@software{door_python_toolkit,
author = {Your Name},
title = {DoOR Python Toolkit},
year = {2025},
url = {https://github.com/yourusername/door-python-toolkit}
}
```
**Original DoOR database:**
```bibtex
@article{muench2016door,
title={DoOR 2.0--Comprehensive Mapping of Drosophila melanogaster Odorant Responses},
author={M{\"u}nch, Daniel and Galizia, C Giovanni},
journal={Scientific Data},
volume={3},
number={1},
pages={1--14},
year={2016},
publisher={Nature Publishing Group}
}
```
---
## License
MIT License - see [LICENSE](LICENSE) file for details.
---
## Troubleshooting
**"Odorant not found"**
→ Use `encoder.list_available_odorants()` to see exact names (case-insensitive)
**"Cache not found"**
→ Run `DoORExtractor` first to extract R data files
**"High sparsity"**
→ Normal for DoOR (86%). Use `fillna(0.0)` or filter to well-covered receptors
**PyTorch not available**
→ Install with `pip install door-python-toolkit[torch]`
---
## Acknowledgments
- DoOR database creators: Daniel Münch & C. Giovanni Galizia
- Original R package: rOpenSci DoOR project
- Contributors: [List contributors]
---
## Links
- **PyPI:** https://pypi.org/project/door-python-toolkit/
- **GitHub:** https://github.com/yourusername/door-python-toolkit
- **Documentation:** https://door-python-toolkit.readthedocs.io
- **Issues:** https://github.com/yourusername/door-python-toolkit/issues
- **Original DoOR:** https://github.com/ropensci/DoOR.data
---
**Made with ❤️ for the *Drosophila* neuroscience community**
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"description": "# DoOR Python Toolkit\n\n[](https://badge.fury.io/py/door-python-toolkit)\n[](https://opensource.org/licenses/MIT)\n[](https://www.python.org/downloads/)\n\n**Python toolkit for working with the DoOR (Database of Odorant Responses) database.**\n\nExtract, analyze, and integrate *Drosophila* odorant-receptor response data in pure Python. No R installation required.\n\n---\n\n## Features\n\n- \u2705 **Pure Python** - Extract DoOR R data files without installing R\n- \ud83d\ude80 **Fast** - Parquet-based caching for quick loading\n- \ud83e\udde0 **ML-Ready** - PyTorch/NumPy integration for neural network training\n- \ud83d\udcca **693 odorants \u00d7 78 receptors** - Comprehensive *Drosophila* olfactory data\n- \ud83d\udd0d **Search & Filter** - Query by odorant name, receptor, or chemical properties\n- \ud83d\udce6 **PyPI Package** - `pip install door-python-toolkit`\n\n---\n\n## Quick Start\n\n### Installation\n\n```bash\npip install door-python-toolkit\n```\n\nFor PyTorch support:\n```bash\npip install door-python-toolkit[torch]\n```\n\n### Extract DoOR Data\n\n```python\nfrom door_toolkit import DoORExtractor\n\n# Extract R data files to Python formats\nextractor = DoORExtractor(\n input_dir=\"path/to/DoOR.data/data\", # Unzipped DoOR R package\n output_dir=\"door_cache\"\n)\nextractor.run()\n```\n\n### Use in Your Code\n\n```python\nfrom door_toolkit import DoOREncoder\n\n# Load encoder\nencoder = DoOREncoder(\"door_cache\")\n\n# Encode single odorant \u2192 78-dim PN activation vector\npn_activation = encoder.encode(\"acetic acid\")\nprint(pn_activation.shape) # (78,)\n\n# Encode batch\nodors = [\"acetic acid\", \"1-pentanol\", \"ethyl acetate\"]\npn_batch = encoder.batch_encode(odors)\nprint(pn_batch.shape) # (3, 78)\n\n# Search odorants\nacetates = encoder.list_available_odorants(pattern=\"acetate\")\nprint(f\"Found {len(acetates)} acetates\") # 36\n\n# Get metadata\nstats = encoder.get_receptor_coverage(\"acetic acid\")\nprint(f\"Active receptors: {stats['n_active']}\")\n```\n\n---\n\n## What is DoOR?\n\nThe **Database of Odorant Responses (DoOR)** is a comprehensive collection of odorant-receptor response measurements for *Drosophila melanogaster* (fruit fly).\n\n**Published:** M\u00fcnch & Galizia (2016), *Scientific Data* 3:160122 \n**Citation:** https://doi.org/10.1038/sdata.2016.122 \n**Original R package:** https://github.com/ropensci/DoOR.data\n\n### Dataset Overview\n\n| Metric | Value |\n|--------|-------|\n| Odorants | 693 compounds |\n| Receptors | 78 ORN types (Or, Ir, Gr) |\n| Measurements | 7,381 odorant-receptor pairs |\n| Sparsity | 86% (typical for chemical screens) |\n| Response Range | [0, 1] normalized |\n\n**Top receptors by coverage:**\n- Or19a: 71.7% (497/693 odorants)\n- Or10a: 33.9% (235/693 odorants)\n- Or22a: 32.5% (225/693 odorants)\n\n---\n\n## Use Cases\n\n### 1. **Neuroscience Research**\nMap odorants to glomerular activation patterns for modeling olfactory processing.\n\n```python\nfrom door_toolkit import DoOREncoder\nimport matplotlib.pyplot as plt\n\nencoder = DoOREncoder(\"door_cache\")\n\n# Compare response profiles\nodors = [\"acetic acid\", \"ethyl acetate\", \"1-pentanol\"]\nresponses = [encoder.encode(o) for o in odors]\n\nplt.figure(figsize=(10, 4))\nfor i, (odor, resp) in enumerate(zip(odors, responses)):\n plt.subplot(1, 3, i+1)\n plt.bar(range(len(resp)), resp)\n plt.title(odor)\n plt.xlabel(\"Receptor\")\n plt.ylabel(\"Response\")\nplt.tight_layout()\nplt.show()\n```\n\n### 2. **Machine Learning**\nTrain neural networks with empirical odorant-receptor data.\n\n```python\nfrom door_toolkit import DoOREncoder\nimport torch\nimport torch.nn as nn\n\n# Load encoder\nencoder = DoOREncoder(\"door_cache\", use_torch=True)\n\n# Create dataset\nclass OdorDataset(torch.utils.data.Dataset):\n def __init__(self, odor_names, labels):\n self.encoder = DoOREncoder(\"door_cache\")\n self.odor_names = odor_names\n self.labels = labels\n \n def __len__(self):\n return len(self.odor_names)\n \n def __getitem__(self, idx):\n pn_activation = self.encoder.encode(self.odor_names[idx])\n return pn_activation, self.labels[idx]\n\n# Train model\nmodel = nn.Sequential(\n nn.Linear(78, 256), # 78 = n_receptors\n nn.ReLU(),\n nn.Linear(256, 2) # Binary classification\n)\n\n# ... standard PyTorch training loop\n```\n\n### 3. **Chemical Similarity**\nFind odorants with similar receptor response patterns.\n\n```python\nfrom door_toolkit.utils import find_similar_odorants\n\nsimilar = find_similar_odorants(\n target_odor=\"acetic acid\",\n cache_path=\"door_cache\",\n top_k=5,\n method=\"correlation\"\n)\n\nfor name, similarity in similar:\n print(f\"{name}: {similarity:.3f}\")\n```\n\n### 4. **Data Analysis**\nExport subsets for custom analyses.\n\n```python\nfrom door_toolkit.utils import export_subset, list_odorants\n\n# Export all acetates\nacetates = list_odorants(\"door_cache\", pattern=\"acetate\")\nexport_subset(\n cache_path=\"door_cache\",\n output_path=\"acetates.csv\",\n odorants=acetates\n)\n```\n\n---\n\n## Command-Line Interface\n\n```bash\n# Extract DoOR data\ndoor-extract --input DoOR.data/data --output door_cache\n\n# Validate cache\npython -c \"from door_toolkit.utils import validate_cache; validate_cache('door_cache')\"\n\n# List odorants\npython -c \"from door_toolkit.utils import list_odorants; print(list_odorants('door_cache', 'acetate'))\"\n```\n\n---\n\n## API Reference\n\n### `DoORExtractor`\nExtract DoOR R data files to Python formats.\n\n**Methods:**\n- `run()` - Execute full extraction pipeline\n- `extract_response_matrix()` - Extract odorant-receptor matrix\n- `extract_odor_metadata()` - Extract chemical properties\n\n### `DoOREncoder`\nEncode odorant names to neural activation patterns.\n\n**Methods:**\n- `encode(odor_name)` - Encode single odorant\n- `batch_encode(odor_names)` - Encode multiple odorants\n- `list_available_odorants(pattern)` - Search odorants\n- `get_receptor_coverage(odor_name)` - Get coverage stats\n- `get_odor_metadata(odor_name)` - Get chemical metadata\n\n### Utilities\nHelper functions in `door_toolkit.utils`:\n\n- `load_response_matrix(cache_path)` - Load response data\n- `load_odor_metadata(cache_path)` - Load metadata\n- `list_odorants(cache_path, pattern)` - List odorants\n- `get_receptor_info(cache_path)` - Receptor statistics\n- `find_similar_odorants(target, cache_path, top_k)` - Similarity search\n- `export_subset(cache_path, output_path, odorants, receptors)` - Export data\n- `validate_cache(cache_path)` - Validate cache integrity\n\n---\n\n## Installation from Source\n\n```bash\n# Clone repository\ngit clone https://github.com/yourusername/door-python-toolkit.git\ncd door-python-toolkit\n\n# Install in development mode\npip install -e .[dev]\n\n# Run tests\npytest tests/\n\n# Format code\nblack door_toolkit/\n```\n\n---\n\n## Requirements\n\n**Core:**\n- Python \u2265 3.8\n- pyreadr \u2265 0.4.7\n- pandas \u2265 1.5.0\n- numpy \u2265 1.21.0\n- pyarrow \u2265 12.0.0\n\n**Optional:**\n- torch \u2265 2.0.0 (for PyTorch integration)\n\n---\n\n## Data Sources\n\nThis toolkit extracts data from the original DoOR R packages:\n\n- **DoOR.data** - https://github.com/ropensci/DoOR.data\n- **DoOR.functions** - https://github.com/ropensci/DoOR.functions\n\nTo use this toolkit, download and unzip the DoOR R packages:\n\n```bash\n# Download from GitHub\nwget https://github.com/ropensci/DoOR.data/archive/refs/tags/v2.0.0.zip\nunzip v2.0.0.zip\n\n# Extract to Python\npython -c \"from door_toolkit import DoORExtractor; DoORExtractor('DoOR.data-2.0.0/data', 'door_cache').run()\"\n```\n\n---\n\n## Contributing\n\nContributions welcome! Please:\n\n1. Fork the repository\n2. Create a feature branch (`git checkout -b feature/amazing-feature`)\n3. Commit changes (`git commit -m 'Add amazing feature'`)\n4. Push to branch (`git push origin feature/amazing-feature`)\n5. Open a Pull Request\n\n**Development setup:**\n```bash\ngit clone https://github.com/yourusername/door-python-toolkit.git\ncd door-python-toolkit\npip install -e .[dev]\npytest tests/\n```\n\n---\n\n## Citation\n\nIf you use this toolkit in your research, please cite both:\n\n**This toolkit:**\n```bibtex\n@software{door_python_toolkit,\n author = {Your Name},\n title = {DoOR Python Toolkit},\n year = {2025},\n url = {https://github.com/yourusername/door-python-toolkit}\n}\n```\n\n**Original DoOR database:**\n```bibtex\n@article{muench2016door,\n title={DoOR 2.0--Comprehensive Mapping of Drosophila melanogaster Odorant Responses},\n author={M{\\\"u}nch, Daniel and Galizia, C Giovanni},\n journal={Scientific Data},\n volume={3},\n number={1},\n pages={1--14},\n year={2016},\n publisher={Nature Publishing Group}\n}\n```\n\n---\n\n## License\n\nMIT License - see [LICENSE](LICENSE) file for details.\n\n---\n\n## Troubleshooting\n\n**\"Odorant not found\"**\n\u2192 Use `encoder.list_available_odorants()` to see exact names (case-insensitive)\n\n**\"Cache not found\"**\n\u2192 Run `DoORExtractor` first to extract R data files\n\n**\"High sparsity\"**\n\u2192 Normal for DoOR (86%). Use `fillna(0.0)` or filter to well-covered receptors\n\n**PyTorch not available**\n\u2192 Install with `pip install door-python-toolkit[torch]`\n\n---\n\n## Acknowledgments\n\n- DoOR database creators: Daniel M\u00fcnch & C. Giovanni Galizia\n- Original R package: rOpenSci DoOR project\n- Contributors: [List contributors]\n\n---\n\n## Links\n\n- **PyPI:** https://pypi.org/project/door-python-toolkit/\n- **GitHub:** https://github.com/yourusername/door-python-toolkit\n- **Documentation:** https://door-python-toolkit.readthedocs.io\n- **Issues:** https://github.com/yourusername/door-python-toolkit/issues\n- **Original DoOR:** https://github.com/ropensci/DoOR.data\n\n---\n\n**Made with \u2764\ufe0f for the *Drosophila* neuroscience community**\n",
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