SeuratExtend: streamlining single-cell RNA-seq analysis through an integrated and intuitive framework

Area of Science:

• Genomics
• Bioinformatics
• Computational Biology

Background:

• Single-cell RNA sequencing (scRNA-seq) generates complex cellular heterogeneity data.
• The proliferation of analytical tools presents challenges for researchers.
• A unified and accessible platform is needed for scRNA-seq data analysis.

Purpose of the Study:

• To introduce SeuratExtend, an R package designed to streamline scRNA-seq data analysis.
• To integrate essential analytical tools and databases into a user-friendly interface.
• To enhance data visualization and accessibility for complex genomic analyses.

Main Methods:

• Development of SeuratExtend, an R package built on the Seurat framework.
• Integration of functional enrichment, trajectory inference, and gene regulatory network reconstruction tools.
• Incorporation of databases like Gene Ontology and Reactome, and Python tools (scVelo, Palantir, SCENIC) via an R interface.

Main Results:

• SeuratExtend provides a unified R interface for diverse scRNA-seq analyses.
• Demonstrated utility in case studies of tumor-associated high-endothelial venules and autoinflammatory diseases.
• Enhanced data visualization with optimized plotting functions and curated color schemes.

Conclusions:

• SeuratExtend empowers researchers to conduct complex scRNA-seq analyses more efficiently.
• The package makes advanced bioinformatics tools accessible to a broader audience.
• Freely available on GitHub, SeuratExtend serves as a valuable resource for the single-cell genomics community.