Metadata-Version: 2.1
Name: scdef
Version: 0.1.2
Summary: Extract hierarchical signatures of cell state from single-cell data.
Home-page: https://github.com/cbg-ethz/scdef
License: MIT
Author: pedrofale
Author-email: pedro.miguel.ferreira.pf@gmail.com
Requires-Python: >=3.9,<4.0
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Classifier: Programming Language :: Python :: 3.13
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Project-URL: Repository, https://github.com/cbg-ethz/scdef
Description-Content-Type: text/markdown

<div align="left">
  <img src="https://github.com/cbg-ethz/scDEF/raw/main/docs/assets/images/scdef.png", width="300px">
</div>
<p></p>

[![pypi](https://img.shields.io/pypi/v/scdef.svg?style=flat)](https://pypi.python.org/pypi/scdef)
[![build](https://github.com/cbg-ethz/scDEF/actions/workflows/test.yaml/badge.svg)](https://github.com/cbg-ethz/scDEF/actions/workflows/test.yaml)
[![docs](https://github.com/cbg-ethz/scDEF/actions/workflows/docs.yaml/badge.svg)](https://cbg-ethz.github.io/scDEF/)

Deep exponential families for single-cell data. scDEF learns hierarchies of cell states and their gene signatures from scRNA-seq data. The method can be used for dimensionality reduction, visualization, gene signature identification, clustering at multiple levels of resolution, and batch integration. The informed version (iscDEF) can additionally take known gene lists to jointly assign cells to types and find clusters within each type.

To install scDEF and get started, [read the documentation](https://cbg-ethz.github.io/scDEF).

