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

Area of Science:

• Neuroscience
• Computational Biology
• Genomics

Background:

• Single-cell transcriptomic studies require accurate cell type classification and standardized nomenclature for cross-dataset comparisons.
• Existing methods struggle with fine-grained cell type identification and cluster-level annotation standardization, limiting comprehensive brain cellular analysis.

Purpose of the Study:

• To introduce BrainCellR, a robust and accessible pipeline for efficient cell type classification and nomination from single-cell transcriptomic data.
• To enable standardized nomenclature for cell types at the cluster level, facilitating data integration and comparative analysis across studies.

Main Methods:

• Development of the BrainCellR computational pipeline.
• Implementation of a standardized nomenclature system for cell type annotation within clusters.
• Focus on single-cell RNA sequencing data analysis.

Main Results:

• BrainCellR provides efficient and user-friendly cell type classification and nomination.
• The pipeline incorporates a standardized nomenclature system for cluster-level cell type annotations.
• BrainCellR is applicable to brain studies and other tissues with complex cellular compositions.

Conclusions:

• BrainCellR addresses the need for precise cell type classification and consistent nomenclature in single-cell neuroscience.
• The standardized annotation system promotes data integration and deeper insights into cellular landscapes.
• The freely available pipeline (https://github.com/WangLab-SINH/BrainCellR) supports reproducible research.