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The tidyomics ecosystem: Enhancing omic data analyses
William J Hutchison1,2, Timothy J Keyes3,4,
1Walter and Eliza Hall Institute of Medical Research, Division of Bioinformatics, Parkville, VIC 3052, Australia.
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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