CNRS, INSERM, CIML, Centre d'Immunologie de Marseille-Luminy, Aix Marseille Univ, Marseille, France.
Turing Centre for Living Systems (CENTURI), Aix Marseille Univ, Marseille, France.
Sci Rep. 2023 Sep 1;13(1):14377. doi: 10.1038/s41598-023-40959-z.
Single-cell technologies have revolutionised biological research and applications. As they continue to evolve with multi-omics and spatial resolution, analysing single-cell datasets is becoming increasingly complex. For biologists lacking expert data analysis resources, the problem is even more crucial, even for the simplest single-cell transcriptomics datasets. We propose ShIVA, an interface for the analysis of single-cell RNA-seq and CITE-seq data specifically dedicated to biologists. Intuitive, iterative and documented by video tutorials, ShIVA allows biologists to follow a robust and reproducible analysis process, mostly based on the Seurat v4 R package, to fully explore and quantify their dataset, to produce useful figures and tables and to export their work to allow more complex analyses performed by experts.
单细胞技术已经彻底改变了生物学研究和应用。随着它们与多组学和空间分辨率的不断发展,分析单细胞数据集变得越来越复杂。对于缺乏专家数据分析资源的生物学家来说,这个问题更加关键,即使是最简单的单细胞转录组学数据集也是如此。我们提出了 ShIVA,这是一个专门为生物学家设计的单细胞 RNA-seq 和 CITE-seq 数据分析接口。ShIVA 直观、迭代,并通过视频教程进行记录,允许生物学家遵循一个稳健且可重复的分析过程,该过程主要基于 Seurat v4 R 包,以充分探索和量化他们的数据集,生成有用的图形和表格,并导出他们的工作,以允许专家进行更复杂的分析。