The tidyomics ecosystem: Enhancing omic data analyses

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

doi:10.1038/s41592-024-02299-2

BioinformaticsNature MethodsJun 14, 2024

Peer-reviewed journal article

The tidyomics ecosystem: Enhancing omic data analyses

William J. Hutchison iD, Timothy Keyes iD, Helena Lucia Crowell iD, Jacques Serizay iD, Charlotte Soneson iD, Eric S. Davis iD, Noriaki Sato iD, Lambda Moses iD, Boyd Tarlinton iD, Abdullah Al Nahid iD, Miha Kosmač iD, Quentin Clayssen iD, Victor Yuan iD, Wancen Mu iD, Ji-Eun Park iD, Izabela Mamede iD, Min Hyung Ryu iD, Pierre‐Paul Axisa iD, Paulina Paiz iD, Chi-Lam Poon iD, Ming Tang iD, Raphaël Gottardo iD, Martin T. Morgan iD, Stuart A. Lee iD, Michael F. Lawrence, , Stefano Mangiola iD

Abstract

The growth of omic data presents evolving challenges in data manipulation, analysis, and integration. Addressing these challenges, Bioconductor1 provides an extensive community-driven biological data analysis platform. Meanwhile, tidy R programming2 offers a revolutionary standard for data organisation and manipulation. 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 analysing 7.5 million peripheral blood mononuclear cells from the Human Cell Atlas3, spanning six data frameworks and ten analysis tools.

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