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@ARTICLE{Hua:302820,
      author       = {Y. Hua and L. Weng and F. Zhao and F. Rambow$^*$},
      title        = {{S}eurat{E}xtend: streamlining single-cell {RNA}-seq
                      analysis through an integrated and intuitive framework.},
      journal      = {GigaScience},
      volume       = {14},
      issn         = {2047-217X},
      address      = {Oxford},
      publisher    = {Oxford University Press},
      reportid     = {DKFZ-2025-01360},
      pages        = {giaf076},
      year         = {2025},
      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.},
      keywords     = {Single-Cell Analysis: methods / Software / Humans /
                      RNA-Seq: methods / Computational Biology: methods / Gene
                      Regulatory Networks / Sequence Analysis, RNA: methods /
                      Single-Cell Gene Expression Analysis / R package (Other) /
                      Seurat framework (Other) / bioinformatics (Other) /
                      education (Other) / multitool integration (Other) / pathway
                      analysis (Other) / single-cell RNA-seq (Other) /
                      visualization (Other)},
      cin          = {ED01},
      ddc          = {610},
      cid          = {I:(DE-He78)ED01-20160331},
      pnm          = {899 - ohne Topic (POF4-899)},
      pid          = {G:(DE-HGF)POF4-899},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:40627366},
      pmc          = {pmc:PMC12236070},
      doi          = {10.1093/gigascience/giaf076},
      url          = {https://inrepo02.dkfz.de/record/302820},
}