Metadata-Version: 2.1
Name: Annotatability
Version: 0.0.5
Summary: Interpreting single cell data using annotation-trainability analysis
Home-page: https://github.com/nitzanlab/Annotatability
Author: jonathankarin
Author-email: jonathan.karin@mail.huji.ac.il
License: MIT License
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.7
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: numpy
Requires-Dist: scanpy
Requires-Dist: numba
Requires-Dist: pandas
Requires-Dist: scikit-learn
Requires-Dist: scipy
Requires-Dist: seaborn
Requires-Dist: matplotlib
Requires-Dist: pytest
Requires-Dist: torch

Annotatability, a method to identify meaningful patterns in single-cell genomics data through annotation-trainability analysis, which estimates annotation congruence using a rich but often overlooked signal, namely the training dynamics of a deep neural network.
