Metadata-Version: 2.1
Name: sc_var
Version: 1.2.3
Summary: An approch for interpreting disease-associated human variants using single-cell epigenomics
Author: Gefei Z
Author-email: gefeizhao@163.com
Requires-Python: >=3.11,<4.0
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Requires-Dist: episcanpy (>=0.4.0,<0.5.0)
Requires-Dist: gseapy (>=1.1.2,<2.0.0)
Requires-Dist: matplotlib (>=3.8.2,<4.0.0)
Requires-Dist: mygene (>=3.2.2,<4.0.0)
Requires-Dist: scanpy (>=1.9.6,<2.0.0)
Requires-Dist: scdrs (>=1.0.2,<2.0.0)
Requires-Dist: scipy (>=1.11.4,<2.0.0)
Requires-Dist: seaborn (>=0.13.1,<0.14.0)
Description-Content-Type: text/markdown

# SC-VAR

Here we report a human variants interpreter, named SC-VAR. 


# What can sc-var do?

This tool can interpret disease-related risks with whole genome wide (including both coding and non-coding regions) variants from GWAS studies and single-cell data on four layers: risk CREs,risk genes, gene sets, and cell types.




# How to install?

pip install sc-var







## About



![Fig1](https://github.com/user-attachments/assets/ccb0e726-225b-4cfa-a71e-2714c92c76b9)






## Usage

Check https://github.com/gefeiZ/sc_var/tree/main/Vignettes for details



Data request: 

Single cell data &

Peak co-accessibility Data or Peak to gene linkage Data (which could obtained from single cell data using cicero or signac) &

GWAS data 


