UCell: Robust and scalable single-cell gene signature scoring.

Area of Science:

• Computational Biology
• Bioinformatics
• Genomics

Background:

• Single-cell RNA sequencing (scRNA-seq) enables high-resolution analysis of cellular heterogeneity.
• Evaluating gene signatures is crucial for understanding cell states and functions in scRNA-seq data.
• Existing methods for gene signature scoring can be computationally intensive and memory-demanding.

Purpose of the Study:

• To introduce UCell, an R package designed for efficient and robust gene signature evaluation in single-cell datasets.
• To provide a computationally lightweight and fast alternative for analyzing large-scale single-cell data.

Main Methods:

• UCell utilizes the Mann-Whitney U statistic for calculating signature scores.
• The package is implemented in R and can process any single-cell data matrix.
• UCell offers seamless integration with Seurat objects for convenient analysis.

Main Results:

• UCell scores are robust to variations in dataset size and cellular heterogeneity.
• The method demonstrates significantly reduced computing time and memory requirements compared to existing approaches.
• UCell enables rapid processing of large single-cell datasets, often within minutes on standard hardware.

Conclusions:

• UCell provides an efficient, scalable, and robust solution for gene signature analysis in single-cell genomics.
• The package lowers computational barriers, facilitating broader application of gene signature evaluation in single-cell research.
• UCell is readily available on GitHub, promoting accessibility and further development.