The tidyomics ecosystem: Enhancing omic data analyses

Hutchison, W. J.; Keyes, T. J.; Crowell, H. L.; Soneson, C.; Mu, W.; Park, J.-E.; Davis, E. S.; Nahid, A. A.; Tang, M.; Yuan, V.; Axisa, P.-P.; Kitt, J. W.; Poon, C.-L.; Sato, N.; Gottardo, R.; Morgan, M.; Lee, S.; Lawrence, M.; Hicks, S. C.; Nolan, G. P.; Davis, K. L.; Papenfuss, A. T.; Love, M. I.; Mangiola, S.

doi:10.1101/2023.09.10.557072

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bioRxiv · 10.1101/2023.09.10.557072

The tidyomics ecosystem: Enhancing omic data analyses

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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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Hutchison, W. J., Keyes, T. J., Crowell, H. L., Soneson, C., Mu, W., Park, J.-E., Davis, E. S., Nahid, A. A., Tang, M., Yuan, V., Axisa, P.-P., Kitt, J. W., Poon, C.-L., Sato, N., Gottardo, R., Morgan, M., Lee, S., Lawrence, M., Hicks, S. C., Nolan, G. P., Davis, K. L., Papenfuss, A. T., Love, M. I., Mangiola, S.. 2023-09-13. The tidyomics ecosystem: Enhancing omic data analyses. https://doi.org/10.1101/2023.09.10.557072

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