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The rise of omic data brings new challenges in how we handle, analyze, and integrate this information, which is crucial for biological research.
Bioconductor serves as a comprehensive platform for community-driven analysis of biological data, while tidy R programming introduces an innovative approach for organizing and manipulating data.
The tidyomics software ecosystem connects Bioconductor with tidy R practices, aiming to simplify omic analysis and facilitate collaboration across different scientific disciplines, as evidenced by its successful application in analyzing a large dataset from the Human Cell Atlas.
Article Abstract
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.
