pyUCell: Robust and scalable single-cell signature scoring

In single-cell RNA-seq analysis, gene signature (or “module”) scoring constitutes a simple yet powerful approach to evaluate the strength of biological signals – typically associated to a specific cell type or biological process – in a transcriptome.

UCell is a computational method for evaluating gene signatures in single-cell datasets. UCell signature scores, based on the Mann-Whitney U statistic, are robust to dataset size and heterogeneity, and their calculation demands less computing time and memory than other available methods, enabling the processing of large datasets in a few minutes even on machines with limited computing power.

pyUCell is a python implementation for the UCell algorithm, also available for the R programming language (Bioconductor and GitHub)

Getting started

Please see installation instructions below, and refer to the documentation.

Installation

Install the latest release of pyUCell from PyPI:

pip install pyucell

or, for the latest development version:

pip install git+ssh://git@github.com/carmonalab/pyucell.git@master

Test the installation

import pyucell as uc
import scanpy as sc

adata = sc.datasets.pbmc3k()

signatures = {
    'T_cell': ['CD3D', 'CD3E', 'CD2'],
    'B_cell': ['MS4A1', 'CD79A', 'CD79B']
}

uc.compute_ucell_scores(adata, signatures=signatures)

Tutorials and how-to

Have a look at the documentation section; you may start from a basic tutorial or explore some important pyUCell parameters.

For a comparison with the R version of UCell on the same scanpy test data, see this notebook

For an assessment of the running time of pyUCell, and parameters affecting speed see: pyUCell timing

Get help

Please address your questions and bug reports at: UCell issues.

Citation

• UCell and pyUCell: single-cell gene signature scoring for R and Python. Massimo Andreatta & Santiago J Carmona (2026) Bioinformatics - doi.org/10.1093/bioinformatics/btag055

• UCell: robust and scalable single-cell gene signature scoring. Massimo Andreatta & Santiago J Carmona (2021) CSBJ - doi.org/10.1016/j.csbj.2021.06.043

Developer guide for scverse tools

https://github.com/scverse/cookiecutter-scverse?tab=readme-ov-file