Metadata-Version: 2.4
Name: scMethyAnno
Version: 1.2.0
Summary: MethyAnno: An Interpretable Automated Annotation Method Leveraging Multi-scale Information and Metric Learning Framework for scDNAm Data
Author: Yuhang Jia
License: MIT
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Classifier: Intended Audience :: Science/Research
Classifier: Topic :: Scientific/Engineering :: Bio-Informatics
Requires-Python: >=3.8.0
Description-Content-Type: text/markdown
License-File: LICENSE.txt
Requires-Dist: numpy>=1.21.0
Requires-Dist: pandas>=1.3.0
Requires-Dist: scipy>=1.7.0
Requires-Dist: anndata>=0.8.0
Requires-Dist: scanpy>=1.9.0
Requires-Dist: scikit-learn>=1.0.0
Requires-Dist: torch>=1.10.0
Requires-Dist: captum>=0.5.0
Requires-Dist: matplotlib>=3.4.0
Requires-Dist: seaborn>=0.11.0
Dynamic: license-file

MethyAnno is an interpretable deep metric learning framework that leverages multi-scale information for accurate cell type annotation of scDNAm data. Additionally, MethyAnno enables Generalized Category Discovery (GCD) in open-set scenarios by utilizing density-based clustering to automatically estimate the number of novel cell types, while simultaneously deciphering cell-type-specific epigenetic signatures for biological interpretability.
