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| Home > Publications database > SeuratExtend: streamlining single-cell RNA-seq analysis through an integrated and intuitive framework. |
| Home > Publications database > SeuratExtend: streamlining single-cell RNA-seq analysis through an integrated and intuitive framework. |
| Journal Article | DKFZ-2025-01360 |
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2025
Oxford University Press
Oxford
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Please use a persistent id in citations: doi:10.1093/gigascience/giaf076
Abstract: Single-cell RNA sequencing (scRNA-seq) has revolutionized the study of cellular heterogeneity, but the rapid expansion of analytical tools has proven to be both a blessing and a curse, presenting researchers with significant challenges. Here, we present SeuratExtend, a comprehensive R package built upon the widely adopted Seurat framework, which streamlines scRNA-seq data analysis by strategically integrating essential tools and databases. SeuratExtend offers a user-friendly and intuitive interface for performing a wide range of analyses, including functional enrichment, trajectory inference, gene regulatory network reconstruction, and denoising. The package integrates multiple databases, such as Gene Ontology and Reactome, and incorporates popular Python tools like scVelo, Palantir, and SCENIC through a unified R interface. We illustrate SeuratExtend's capabilities through case studies investigating tumor-associated high-endothelial venules and autoinflammatory diseases, as well as showcase its novel applications in pathway-level analysis and cluster annotation. SeuratExtend enhances data visualization with optimized plotting functions and carefully curated color schemes, ensuring both aesthetic appeal and scientific rigor. The package's effectiveness has been demonstrated through successful workshops and training programs, establishing its value in both research and educational contexts. SeuratExtend empowers researchers to harness the full potential of scRNA-seq data, making complex analyses accessible to a wider audience. The package, along with comprehensive documentation, tutorials, and educational resources, is freely available at GitHub, providing a valuable resource for the single-cell genomics community.
Keyword(s): Single-Cell Analysis: methods (MeSH) ; Software (MeSH) ; Humans (MeSH) ; RNA-Seq: methods (MeSH) ; Computational Biology: methods (MeSH) ; Gene Regulatory Networks (MeSH) ; Sequence Analysis, RNA: methods (MeSH) ; Single-Cell Gene Expression Analysis (MeSH) ; R package ; Seurat framework ; bioinformatics ; education ; multitool integration ; pathway analysis ; single-cell RNA-seq ; visualization
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