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The tidyomics ecosystem: enhancing omic data analyses.
Hutchison, William J; Keyes, Timothy J; Crowell, Helena L; Serizay, Jacques; Soneson, Charlotte; Davis, Eric S; Sato, Noriaki; Moses, Lambda; Tarlinton, Boyd; Nahid, Abdullah A; Kosmac, Miha; Clayssen, Quentin; Yuan, Victor; Mu, Wancen; Park, Ji-Eun; Mamede, Izabela; Ryu, Min Hyung; Axisa, Pierre-Paul; Paiz, Paulina; Poon, Chi-Lam; Tang, Ming; Gottardo, Raphael; Morgan, Martin; Lee, Stuart; Lawrence, Michael; Hicks, Stephanie C; Nolan, Garry P; Davis, Kara L; Papenfuss, Anthony T; Love, Michael I; Mangiola, Stefano.
Afiliação
  • Hutchison WJ; The Walter and Eliza Hall Institute of Medical Research, Parkville, Victoria, Australia.
  • Keyes TJ; Department of Medical Biology, University of Melbourne, Parkville, Victoria, Australia.
  • Crowell HL; Department of Pediatrics, Stanford University School of Medicine, Stanford, CA, USA.
  • Soneson C; University of Zurich, Zurich, Switzerland.
  • Davis ES; Centro Nacional de Análisis Genómico (CNAG), Barcelona, Spain.
  • Sato N; Unité Régulation Spatiale des Génomes, Institut Pasteur, CNRS UMR3525, Paris, France.
  • Moses L; Friedrich Miescher Institute for Biomedical Research, Basel, Switzerland.
  • Tarlinton B; SIB Swiss Institute of Bioinformatics, Basel, Switzerland.
  • Nahid AA; Bioinformatics and Computational Biology Program, University of North Carolina-Chapel Hill, Chapel Hill, NC, USA.
  • Kosmac M; Division of Health Medical Intelligence, Human Genome Center, The Institute of Medical Science, The University of Tokyo, Tokyo, Japan.
  • Clayssen Q; California Institute of Technology, Pasadena, CA, USA.
  • Yuan V; Queensland Department of Agriculture and Fisheries, Brisbane, Queensland, Australia.
  • Mu W; Department of Biochemistry and Molecular Biology, Shahjalal University of Science and Technology, Sylhet, Bangladesh.
  • Park JE; Achilles Therapeutics, London, UK.
  • Mamede I; DNA Script, Le Kremlin-Bicêtre, France.
  • Ryu MH; Department of Statistics, The University of British Columbia, Vancouver, British Columbia, Canada.
  • Axisa PP; Biostatistics Department, University of North Carolina-Chapel Hill, Chapel Hill, NC, USA.
  • Paiz P; Biostatistics Department, University of North Carolina-Chapel Hill, Chapel Hill, NC, USA.
  • Poon CL; Department of Biochemistry and Immunology, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil.
  • Tang M; Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA.
  • Gottardo R; Department of Medicine, Harvard Medical School, Boston, MA, USA.
  • Morgan M; Centre de Recherches en Cancérologie de Toulouse, Université de Toulouse, Inserm, CNRS, Université Toulouse III-Paul Sabatier, Toulouse, France.
  • Lee S; Department of Biomedical Data Science, Stanford University School of Medicine, Stanford, CA, USA.
  • Lawrence M; Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY, USA.
  • Hicks SC; Immunitas Therapeutics, Waltham, MA, USA.
  • Nolan GP; SIB Swiss Institute of Bioinformatics, Basel, Switzerland.
  • Davis KL; University of Lausanne, Lausanne, Switzerland.
  • Papenfuss AT; Lausanne University Hospital, Lausanne, Switzerland.
  • Love MI; Roswell Park Comprehensive Cancer Center, Buffalo, NY, USA.
  • Mangiola S; Department of Bioinformatics and Computational Biology, Genentech, South San Francisco, CA, USA.
Nat Methods ; 21(7): 1166-1170, 2024 Jul.
Article em En | MEDLINE | ID: mdl-38877315
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.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Software Limite: Humans Idioma: En Ano de publicação: 2024 Tipo de documento: Article
Texto completo
Imprimir
XML
PubMed Links
Buscar no Google

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Software Limite: Humans Idioma: En Ano de publicação: 2024 Tipo de documento: Article