AutoGeneS automatically extracts informative genes and reveals the cellular heterogeneity of bulk RNA samples. AutoGeneS requires no prior knowledge about marker genes and selects genes by simultaneously optimizing multiple criteria: minimizing the correlation and maximizing the distance between cell types. It can be applied to reference profiles from various sources like single-cell experiments or sorted cell populations.
For a multi-objective optimization problem, there usually exists no single solution that simultaneously optimizes all objectives. In this case, the objective functions are said to be conflicting, and there exists a (possibly infinite) number of Pareto-optimal solutions. Pareto-(semi)optimal solutions are a set of all solutions that are not dominated by any other explored solution. Pareto-optimal solutions offer a set of equally good solutions from which to select, depending on the dataset
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PyPI only
pip install autogenes -
Development Version (latest version on github)
git clone https://github.com/theislab/AutoGeneS
pip install dist/autogenes-1.0.4-py3-none-any.whl
- python>=3.6
- pandas>=0.25.1
- anndata>=0.6.22.post1
- numpy>=1.17.2
- dill>=0.3.1.1
- deap>=1.3.0
- scipy>=1.3
- cachetools>=3.1.1
- scikit-learn>=0.21.3
- matplotlib>=3.0