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 MamedeMin Hyung RyuPierre-Paul AxisaPaulina PaizChi-Lam PoonMing TangRaphaël GottardoMartin MorganStuart LeeMichael LawrenceStephanie C HicksGarry P NolanKara L DavisAnthony T PapenfussMichael I LoveStefano MangiolaPublished in: bioRxiv : the preprint server for biology (2024)
The growth of omic data presents evolving challenges in data manipulation, analysis, and integration. Addressing these challenges, Bioconductor 1 provides an extensive community-driven biological data analysis platform. Meanwhile, tidy R programming 2 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 Atlas 3 , spanning six data frameworks and ten analysis tools.