Metadata-Version: 2.1
Name: scperturb
Version: 0.0.6
Summary: Python package with E-distance tools for single-cell perturbation data analysis.
Home-page: https://github.com/sanderlab/scPerturb
Author: Stefan Peidli
Author-email: stefanpeidli@gmail.com
Project-URL: Bug Tracker, https://github.com/sanderlab/scPerturb/issues
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Science/Research
Classifier: Natural Language :: English
Classifier: Topic :: Scientific/Engineering :: Bio-Informatics
Requires-Python: >=3.6
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: pandas >=1.1
Requires-Dist: scanpy >=1.7
Requires-Dist: tqdm >=4.62
Requires-Dist: numpy
Requires-Dist: scipy
Requires-Dist: scikit-learn
Requires-Dist: statsmodels

# scperturb
A python package to compute E-distances in single-cell perturbation data and
perform E-tests.

# Install
Just install via pip:

```
pip install scperturb
```

# Usage example

```
# E-distances
estats = edist(adata, obs_key='perturbation')
# E-distances to a specific group (e.g. 'control')
estats_control = estats.loc['control']
# E-test for difference to control
df = etest(adata, obs_key='perturbation', obsm_key='X_pca', dist='sqeuclidean', control='control', alpha=0.05, runs=100)
```

Check out notebooks --> e-distance for tutorial.
