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The tidyomics ecosystem: enhancing omic data analyses

  • William J. Hutchison,
  • Timothy J. Keyes,
  • Helena L. Crowell,
  • Jacques Serizay,
  • Charlotte Soneson,
  • Eric S. Davis,
  • Noriaki Sato,
  • Lambda Moses,
  • Boyd Tarlinton,
  • Abdullah A. Nahid,
  • Miha Kosmac,
  • Quentin Clayssen,
  • Victor Yuan,
  • Wancen Mu,
  • Ji-Eun Park,
  • Izabela Mamede,
  • Min Hyung Ryu,
  • Pierre-Paul Axisa,
  • Paulina Paiz,
  • Chi-Lam Poon,
  • Ming Tang,
  • Raphael Gottardo,
  • Martin Morgan,
  • Stuart Lee,
  • Michael Lawrence,
  • Stephanie C. Hicks,
  • Garry P. Nolan,
  • Kara L. Davis,
  • Anthony T. Papenfuss,
  • Michael I. Love,
  • Stefano Mangiola

摘要

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.