ShIVA: a user-friendly and interactive interface giving biologists control over their single-cell RNA-seq data
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 cruc...
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| Published in | Scientific reports Vol. 13; no. 1; pp. 14377 - 7 |
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| Main Authors | , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
London
Nature Publishing Group UK
01.09.2023
Nature Publishing Group Nature Portfolio |
| Subjects | |
| Online Access | Get full text |
| ISSN | 2045-2322 2045-2322 |
| DOI | 10.1038/s41598-023-40959-z |
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| Abstract | 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. |
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| AbstractList | 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. 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.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. Abstract 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. |
| ArticleNumber | 14377 |
| Author | Chenag, Sabrina Jaeger, Sébastien Spinelli, Lionel Milpied, Pierre Aussel, Rudy Asif, Muhammad |
| Author_xml | – sequence: 1 givenname: Rudy surname: Aussel fullname: Aussel, Rudy organization: CNRS, INSERM, CIML, Centre d’Immunologie de Marseille-Luminy, Aix Marseille Univ – sequence: 2 givenname: Muhammad surname: Asif fullname: Asif, Muhammad organization: Turing Centre for Living Systems (CENTURI), Aix Marseille Univ, Biomedical Data Science Lab, Department of Bioinformatics and Biotechnology, Government College University Faisalabad (GCUF) – sequence: 3 givenname: Sabrina surname: Chenag fullname: Chenag, Sabrina organization: CNRS, INSERM, CIML, Centre d’Immunologie de Marseille-Luminy, Aix Marseille Univ – sequence: 4 givenname: Sébastien surname: Jaeger fullname: Jaeger, Sébastien organization: CNRS, INSERM, CIML, Centre d’Immunologie de Marseille-Luminy, Aix Marseille Univ, Turing Centre for Living Systems (CENTURI), Aix Marseille Univ – sequence: 5 givenname: Pierre surname: Milpied fullname: Milpied, Pierre email: milpied@ciml.univ-mrs.fr organization: CNRS, INSERM, CIML, Centre d’Immunologie de Marseille-Luminy, Aix Marseille Univ, Turing Centre for Living Systems (CENTURI), Aix Marseille Univ – sequence: 6 givenname: Lionel surname: Spinelli fullname: Spinelli, Lionel email: spinelli@ciml.univ-mrs.fr organization: CNRS, INSERM, CIML, Centre d’Immunologie de Marseille-Luminy, Aix Marseille Univ, Turing Centre for Living Systems (CENTURI), Aix Marseille Univ |
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| CitedBy_id | crossref_primary_10_1093_bioadv_vbae062 crossref_primary_10_1038_s41467_024_51164_5 crossref_primary_10_1520_JTE20230051 |
| Cites_doi | 10.18637/jss.v022.i04 10.1093/nar/gkac322 10.1089/omi.2011.0118 10.1186/s13059-018-1603-1 10.1038/s41592-021-01102-w 10.1038/s41598-019-41695-z 10.1093/database/baaa073 10.1038/s41587-020-0449-8 10.1038/d41586-021-01994-w 10.1038/s41467-022-29744-0 10.21105/joss.00861 10.1016/j.cell.2021.04.048 10.1038/s41592-019-0433-8 |
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| Keywords | Genomics Software |
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| Snippet | Single-cell technologies have revolutionised biological research and applications. As they continue to evolve with multi-omics and spatial resolution,... Abstract Single-cell technologies have revolutionised biological research and applications. As they continue to evolve with multi-omics and spatial resolution,... |
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| Title | ShIVA: a user-friendly and interactive interface giving biologists control over their single-cell RNA-seq data |
| URI | https://link.springer.com/article/10.1038/s41598-023-40959-z https://www.proquest.com/docview/2859762334 https://www.proquest.com/docview/2860403316 https://amu.hal.science/hal-04534416 https://pubmed.ncbi.nlm.nih.gov/PMC10474127 https://doaj.org/article/65d5944731514148b011c5306d696180 |
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