https://github.com/lcwheeler/orbweaver
Orbweaver is a python package for performing gene co-expression network analysis
https://github.com/lcwheeler/orbweaver
Last synced: 2 months ago
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Orbweaver is a python package for performing gene co-expression network analysis
- Host: GitHub
- URL: https://github.com/lcwheeler/orbweaver
- Owner: lcwheeler
- License: mit
- Created: 2018-05-15T21:03:25.000Z (about 7 years ago)
- Default Branch: master
- Last Pushed: 2019-05-23T21:05:13.000Z (about 6 years ago)
- Last Synced: 2025-02-08T06:12:41.872Z (4 months ago)
- Language: Jupyter Notebook
- Size: 237 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# orbweaver
Orbweaver is a simple python API for performing gene co-expression network analysis. It's a pet project and it's currently still under development. Pull requests are welcome. However, this package is low priority, so I may not be extremely efficient at responding to contributors. Orbweaver relies heavily on the wonderful [Networkx](https://networkx.github.io/) API. It is thus compatible with the wide range of network analysis tools available in Networkx. Other key dependencies are listed in the setup.py. A demonstration of the current Orbweaver workflow can be found in the *notebooks/demo.ipynb* Jupyter notebook.
## Current features include:
1. Straightforward API for handling datasets, matrices, and network objects.
2. Multiple similarity metrics for comparing gene expression profiles.
3. Simple built-in visualizations for similarity matrices and networks.
4. Functions for re-weighting graphs and converting from weighted to unweighted.
5. Exporting of network objects in various formats.
## Future features (in development) will include:
1. Simulation of networks and data with specified structure and co-expression relationships.
2. Improved network visualization.
3. Tools for handling uncertainty across replicate datasets.
4. Built-in approaches for data clustering.
5. Functions for calculating certain aspects of network topology.
## Install
Clone this repository and install a development version using `pip`:
```
pip install -e .
```