autogenes 1.0.4

Automatic Gene Selection for Bulk Deconvolution

AutoGeneS

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

Installation

1. PyPI only
   pip install autogenes
   

2. Development Version (latest version on github)
   git clone https://github.com/theislab/AutoGeneS
pip install dist/autogenes-1.0.3-py3-none-any.whl
   

Example

Example on pseudo bulks

Documentation

Documentation

Getting Started

Dependencies

• 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

Citation

Aliee, Hananeh and Theis, Fabian, AutoGeneS: Automatic gene selection using multi-objective optimization for RNA-seq deconvolution