Metadata-Version: 2.1
Name: scmer
Version: 0.1.0a2
Summary: Manifold preserving marker selection for single-cell data
Home-page: https://scmer.readthedocs.io/
Author: Shaoheng Liang
Author-email: 
License: UNKNOWN
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.6
Description-Content-Type: text/markdown
Requires-Dist: torch
Requires-Dist: scikit-learn (==0.23.2)
Requires-Dist: numpy
Requires-Dist: scipy
Requires-Dist: pandas
Requires-Dist: seaborn

# SCMER - Manifold Preserving Feature Selection 
[![Documentation Status](https://readthedocs.org/projects/scmer/badge/?version=latest)](https://scmer.readthedocs.io/en/latest/?badge=latest) [![PyPI](https://img.shields.io/pypi/v/scmer-lshh125?color=blue&logo=pypi)](https://pypi.org/project/scmer)

SCMER is a feature selection methods designed for single-cell data analysis. 
It selects a compact sets of markers that preserve the manifold in the original data.
It can also be used for data integration by using features in one modality to match the manifold of another modality.

## Tutorials ##
Tutorials are available at https://scmer.readthedocs.io/en/latest/examples.html

You may start with the [Melanoma data (Tiorsh et al.)](https://scmer.readthedocs.io/en/latest/melanoma.html).

## Full Documentation ##
Detailed documentation is available at https://scmer.readthedocs.io/en/latest/

Preprint: https://www.biorxiv.org/content/10.1101/2020.12.01.407262v1.full

The mechanism and capabilities of SCMER is detailed in our pre-print [Single-Cell Manifold Preserving Feature Selection (SCMER)](https://www.biorxiv.org/content/10.1101/2020.12.01.407262v1)


