Optimal marker gene selection for cell type discrimination in single cell analyses
The selection of a small set of cellular labels to distinguish a subpopulation of cells from a complex mixture is an important task in cell biology. Here the authors propose a method for supervised genetic marker selection using linear programming and provides a Python package scGeneFit that impleme...
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| Autores principales: | , , , |
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| Formato: | article |
| Lenguaje: | EN |
| Publicado: |
Nature Portfolio
2021
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| Materias: | |
| Acceso en línea: | https://doaj.org/article/a73a6f0b9b614243a59329f665ef0fa6 |
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| Sumario: | The selection of a small set of cellular labels to distinguish a subpopulation of cells from a complex mixture is an important task in cell biology. Here the authors propose a method for supervised genetic marker selection using linear programming and provides a Python package scGeneFit that implements this approach. |
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