The tidyomics ecosystem: enhancing omic data analyses.
William J HutchisonTimothy J Keyesnull nullHelena Lucia CrowellJacques SerizayCharlotte SonesonEric S DavisNoriaki SatoLambda MosesBoyd TarlintonAbdullah Al NahidMiha KosmacQuentin ClayssenVictor YuanWancen MuJi-Eun ParkIzabela Mamede C A da ConceiçãoMin Hyung RyuPierre-Paul AxisaPaulina PaizChi-Lam PoonMing TangRaphaël GottardoMartin MorganStuart LeeMichael LawrenceStephanie C HicksGarry P NolanKara L DavisAnthony T PapenfussMichael I LoveStefano MangiolaPublished in: Nature methods (2024)
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.