Metadata-Version: 2.5
Name: scparadise
Version: 1.2.0
Summary: Single-cell RNA-seq data analysis using machine-learning tools.
Project-URL: Homepage, https://github.com/Chechekhins/scParadise
Project-URL: Source, https://github.com/Chechekhins/scParadise
Project-URL: Issues, https://github.com/Chechekhins/scParadise/issues
Author: Vadim Chechekhin, Elizaveta Chechekhina
Maintainer-email: Vadim Chechekhin <vadimchex97@gmail.com>, Elizaveta Chechekhina <voynovaes.pharm@gmail.com>
License: MIT
License-File: LICENSE.txt
Classifier: Development Status :: 5 - Production/Stable
Classifier: Environment :: GPU
Classifier: Framework :: Jupyter
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: MIT License
Classifier: Natural Language :: English
Classifier: Operating System :: Microsoft :: Windows
Classifier: Operating System :: POSIX :: Linux
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: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Topic :: Scientific/Engineering :: Bio-Informatics
Requires-Python: <3.13,>=3.9
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Requires-Dist: anndata>=0.10
Requires-Dist: fsspec>=2024.2.0
Requires-Dist: imbalanced-learn>=0.11
Requires-Dist: matplotlib>=3.6
Requires-Dist: mousipy>=0.1.0
Requires-Dist: muon>=0.1.6
Requires-Dist: numba>=0.57
Requires-Dist: numpy>=1.26
Requires-Dist: optuna>=3.4
Requires-Dist: packaging>=21.3
Requires-Dist: pandas>=1.5
Requires-Dist: plottable>=0.1.4
Requires-Dist: pytorch-tabnet>=4.0
Requires-Dist: requests>=2.28
Requires-Dist: scanpy>=1.10
Requires-Dist: scikit-image>=0.23
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Requires-Dist: scipy>=1.10
Requires-Dist: seaborn>=0.12
Requires-Dist: shap<0.50,>=0.46
Requires-Dist: torch>=2.0
Requires-Dist: tqdm>=4.36
Description-Content-Type: text/markdown

# scParadize
Python tool for accurate, reproducible scRNA-seq analysis:
1) Multilevel cell type annotation with unknown cell type identification using scAdam models
2) Modality imputation using scEve models
3) Benchmarking using scNoah
