The tidyomics ecosystem: enhancing omic data analyses
The growth of omic data presents evolving challenges in data manipulation, analysis and integration. Addressing these challenges, Bioconductor provides an extensive community-driven biological data analysis platform. Meanwhile, tidy R programming offers a revolutionary data organization and manipula...
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| Published in | Nature methods Vol. 21; no. 7; pp. 1166 - 1170 |
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| Main Authors | , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
New York
Nature Publishing Group US
01.07.2024
Nature Publishing Group |
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| Online Access | Get full text |
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| Abstract | The growth of omic data presents evolving challenges in data manipulation, analysis and integration. Addressing these challenges, Bioconductor provides an extensive community-driven biological data analysis platform. Meanwhile, tidy R programming offers a revolutionary data organization and manipulation standard. Here we present the tidyomics software ecosystem, bridging Bioconductor to the tidy R paradigm. This ecosystem aims to streamline omic analysis, ease learning and encourage cross-disciplinary collaborations. We demonstrate the effectiveness of tidyomics by analyzing 7.5 million peripheral blood mononuclear cells from the Human Cell Atlas, spanning six data frameworks and ten analysis tools.
tidyomics offers a software ecosystem for omic data manipulation and analysis that bridges Bioconductor with the tidyverse framework. |
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| AbstractList | The growth of omic data presents evolving challenges in data manipulation, analysis and integration. Addressing these challenges, Bioconductor provides an extensive community-driven biological data analysis platform. Meanwhile, tidy R programming offers a revolutionary data organization and manipulation standard. Here we present the tidyomics software ecosystem, bridging Bioconductor to the tidy R paradigm. This ecosystem aims to streamline omic analysis, ease learning and encourage cross-disciplinary collaborations. We demonstrate the effectiveness of tidyomics by analyzing 7.5 million peripheral blood mononuclear cells from the Human Cell Atlas, spanning six data frameworks and ten analysis tools.
tidyomics offers a software ecosystem for omic data manipulation and analysis that bridges Bioconductor with the tidyverse framework. The growth of omic data presents evolving challenges in data manipulation, analysis and integration. Addressing these challenges, Bioconductor provides an extensive community-driven biological data analysis platform. Meanwhile, tidy R programming offers a revolutionary data organization and manipulation standard. Here we present the tidyomics software ecosystem, bridging Bioconductor to the tidy R paradigm. This ecosystem aims to streamline omic analysis, ease learning and encourage cross-disciplinary collaborations. We demonstrate the effectiveness of tidyomics by analyzing 7.5 million peripheral blood mononuclear cells from the Human Cell Atlas, spanning six data frameworks and ten analysis tools. The growth of omic data presents evolving challenges in data manipulation, analysis and integration. Addressing these challenges, Bioconductor provides an extensive community-driven biological data analysis platform. Meanwhile, tidy R programming offers a revolutionary data organization and manipulation standard. Here we present the tidyomics software ecosystem, bridging Bioconductor to the tidy R paradigm. This ecosystem aims to streamline omic analysis, ease learning and encourage cross-disciplinary collaborations. We demonstrate the effectiveness of tidyomics by analyzing 7.5 million peripheral blood mononuclear cells from the Human Cell Atlas, spanning six data frameworks and ten analysis tools.tidyomics offers a software ecosystem for omic data manipulation and analysis that bridges Bioconductor with the tidyverse framework. The growth of omic data presents evolving challenges in data manipulation, analysis and integration. Addressing these challenges, Bioconductor provides an extensive community-driven biological data analysis platform. Meanwhile, tidy R programming offers a revolutionary data organization and manipulation standard. Here we present the tidyomics software ecosystem, bridging Bioconductor to the tidy R paradigm. This ecosystem aims to streamline omic analysis, ease learning and encourage cross-disciplinary collaborations. We demonstrate the effectiveness of tidyomics by analyzing 7.5 million peripheral blood mononuclear cells from the Human Cell Atlas, spanning six data frameworks and ten analysis tools.The growth of omic data presents evolving challenges in data manipulation, analysis and integration. Addressing these challenges, Bioconductor provides an extensive community-driven biological data analysis platform. Meanwhile, tidy R programming offers a revolutionary data organization and manipulation standard. Here we present the tidyomics software ecosystem, bridging Bioconductor to the tidy R paradigm. This ecosystem aims to streamline omic analysis, ease learning and encourage cross-disciplinary collaborations. We demonstrate the effectiveness of tidyomics by analyzing 7.5 million peripheral blood mononuclear cells from the Human Cell Atlas, spanning six data frameworks and ten analysis tools. |
| Author | Ryu, Min Hyung Kosmac, Miha Crowell, Helena L. Tang, Ming Papenfuss, Anthony T. Serizay, Jacques Love, Michael I. Poon, Chi-Lam Gottardo, Raphael Davis, Kara L. Clayssen, Quentin Mu, Wancen Sato, Noriaki Moses, Lambda Nahid, Abdullah A. Axisa, Pierre-Paul Lawrence, Michael Nolan, Garry P. Tarlinton, Boyd Davis, Eric S. Paiz, Paulina Yuan, Victor Keyes, Timothy J. Park, Ji-Eun Mangiola, Stefano Soneson, Charlotte Lee, Stuart Hicks, Stephanie C. Mamede, Izabela Hutchison, William J. Morgan, Martin |
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for Biomedical Research, SIB Swiss Institute of Bioinformatics – sequence: 7 givenname: Eric S. orcidid: 0000-0003-4051-3217 surname: Davis fullname: Davis, Eric S. organization: Bioinformatics and Computational Biology Program, University of North Carolina-Chapel Hill – sequence: 8 givenname: Noriaki orcidid: 0000-0001-7721-9359 surname: Sato fullname: Sato, Noriaki organization: Division of Health Medical Intelligence, Human Genome Center, The Institute of Medical Science, The University of Tokyo – sequence: 9 givenname: Lambda orcidid: 0000-0002-7092-9427 surname: Moses fullname: Moses, Lambda organization: California Institute of Technology – sequence: 10 givenname: Boyd orcidid: 0000-0002-4146-7083 surname: Tarlinton fullname: Tarlinton, Boyd organization: Queensland Department of Agriculture and Fisheries – sequence: 11 givenname: Abdullah A. orcidid: 0000-0002-4390-0768 surname: Nahid fullname: Nahid, Abdullah A. organization: Department of Biochemistry and Molecular Biology, 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18 givenname: Min Hyung orcidid: 0000-0003-3071-7363 surname: Ryu fullname: Ryu, Min Hyung organization: Channing Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital, Department of Medicine, Harvard Medical School – sequence: 19 givenname: Pierre-Paul surname: Axisa fullname: Axisa, Pierre-Paul organization: Centre de Recherches en Cancérologie de Toulouse, Université de Toulouse, Inserm, CNRS, Université Toulouse III-Paul Sabatier – sequence: 20 givenname: Paulina orcidid: 0009-0009-1766-7032 surname: Paiz fullname: Paiz, Paulina organization: Department of Biomedical Data Science, Stanford University School of Medicine – sequence: 21 givenname: Chi-Lam orcidid: 0000-0001-6298-7099 surname: Poon fullname: Poon, Chi-Lam organization: Department of Physiology and Biophysics, Weill Cornell Medicine – sequence: 22 givenname: Ming orcidid: 0000-0002-7739-8913 surname: Tang fullname: Tang, Ming organization: Immunitas Therapeutics – sequence: 23 givenname: Raphael orcidid: 0000-0002-3867-0232 surname: Gottardo fullname: Gottardo, Raphael organization: SIB Swiss Institute of Bioinformatics, University of Lausanne, Lausanne University Hospital – sequence: 24 givenname: Martin orcidid: 0000-0002-5874-8148 surname: Morgan fullname: Morgan, Martin organization: Roswell Park Comprehensive Cancer Center – sequence: 25 givenname: Stuart orcidid: 0000-0003-1179-8436 surname: Lee fullname: Lee, Stuart organization: Department of Bioinformatics and Computational Biology, Genentech – sequence: 26 givenname: Michael surname: Lawrence fullname: Lawrence, Michael organization: Department of Bioinformatics and Computational Biology, Genentech – sequence: 27 givenname: Stephanie C. orcidid: 0000-0002-7858-0231 surname: Hicks fullname: Hicks, Stephanie C. organization: Department of Biostatistics, Johns Hopkins University, Department of Biomedical Engineering, Johns Hopkins University, Malone Center for Engineering in Healthcare, Johns Hopkins University 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Mangiola fullname: Mangiola, Stefano email: stefano.mangiola@adelaide.edu.au organization: The Walter and Eliza Hall Institute of Medical Research, Department of Medical Biology, University of Melbourne, South Australian immunoGENomics Cancer Institute, The University of Adelaide |
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| CitedBy_id | crossref_primary_10_1016_j_cell_2025_02_018 crossref_primary_10_3390_genes15121551 crossref_primary_10_1093_bioinformatics_btae487 crossref_primary_10_1016_j_cofs_2024_101253 crossref_primary_10_3390_genes15091185 |
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| Copyright | Crown 2024 2024. Crown. Crown 2024. |
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| DOI | 10.1038/s41592-024-02299-2 |
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