BrainCellR: A precise cell type nomenclature pipeline for comparative analysis across brain single-cell datasets

Abstract

Single-cell studies in neuroscience require precise cell type classification and consistent nomenclature that allows for meaningful comparisons across diverse datasets. Current approaches often lack the ability to identify fine-grained cell types and establish standardized annotations at the cluster level, hindering comprehensive understanding of the brain's cellular composition. To facilitate data integration across multiple models and datasets, we designed BrainCellR. This pipeline provides researchers with a powerful and user-friendly tool for efficient cell type classification and nomination from single-cell transcriptomic data. While initially focused on brain studies, BrainCellR is applicable to other tissues with complex cellular compositions. BrainCellR goes beyond conventional classification approaches by incorporating a standardized nomenclature system for cell types at the cluster level. This feature enables consistent and comparable annotations across different studies, promoting data integration and providing deeper insights into the complex cellular landscape of the brain. All documents for BrainCellR, including source code, user manual and tutorials, are freely available at https://github.com/WangLab-SINH/BrainCellR.

Keywords

• Brain
• ScRNA-seq
• Cell type annotation
• R package
• Comparison between datasets