Hua Yichao, Weng Linqian, Zhao Fang, Rambow Florian
Department of Applied Computational Cancer Research, Institute for AI in Medicine (IKIM), University Hospital Essen, Essen 45131, Germany.
University Duisburg-Essen, Essen 45141, Germany.
Gigascience. 2025 Jan 6;14. doi: 10.1093/gigascience/giaf076.
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.
单细胞RNA测序（scRNA-seq）彻底改变了细胞异质性的研究，但分析工具的迅速扩展已被证明既是幸事也是麻烦事，给研究人员带来了重大挑战。在这里，我们展示了SeuratExtend，这是一个基于广泛采用的Seurat框架构建的综合R包，它通过战略性地整合基本工具和数据库来简化scRNA-seq数据分析。SeuratExtend提供了一个用户友好且直观的界面，用于执行广泛的分析，包括功能富集、轨迹推断、基因调控网络重建和去噪。该包整合了多个数据库，如基因本体论（Gene Ontology）和Reactome，并通过统一的R接口纳入了scVelo、Palantir和SCENIC等流行的Python工具。我们通过研究肿瘤相关的高端内皮微静脉和自身炎症性疾病的案例研究来说明SeuratExtend的功能，并展示其在通路水平分析和聚类注释中的新应用。SeuratExtend通过优化的绘图函数和精心策划的配色方案增强了数据可视化，确保了美观性和科学严谨性。该包的有效性已通过成功举办的研讨会和培训项目得到证明，确立了其在研究和教育环境中的价值。SeuratExtend使研究人员能够充分利用scRNA-seq数据的全部潜力，使更广泛的受众能够进行复杂的分析。该包连同全面的文档、教程和教育资源可在GitHub上免费获取，为单细胞基因组学社区提供了宝贵的资源。
