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Updated: Feb 12, 2026

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Reconstruction of Single-Cell Innate Fluorescence Signatures by Confocal Microscopy
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UCell and pyUCell: single-cell gene signature scoring for R and Python.
Massimo Andreatta1,2,3,4, Santiago J Carmona1,2,3,4
1Department of Pathology and Immunology, Faculty of Medicine, University of Geneva, Geneva 1206, Switzerland.
Bioinformatics (Oxford, England)
|February 10, 2026
Summary
Gene signature scoring quantifies biological signals in single-cell omics data. UCell (R) and pyUCell (Python) provide fast, robust tools for this analysis, integrating with major platforms.
Area of Science:
- Computational biology
- Single-cell omics analysis
Background:
- Single-cell omics technologies generate high-dimensional data.
- Quantifying biological signals within these datasets is crucial for discovery.
Purpose of the Study:
- To introduce UCell and pyUCell as efficient tools for gene signature scoring.
- To enable robust quantification of biological signals in single-cell omics data.
Main Methods:
- Rank-based gene signature scoring implemented in R (UCell) and Python (pyUCell).
- Integration with popular single-cell analysis workflows like Seurat, Bioconductor, and scanpy/scverse.
Main Results:
- UCell and pyUCell offer fast and robust performance for gene signature scoring.
- These tools facilitate seamless integration into existing single-cell analysis pipelines.
Conclusions:
- Gene signature scoring is a powerful method for analyzing single-cell omics data.
- UCell and pyUCell provide accessible and efficient implementations for researchers.
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