Contact

  • Academic
    ppaiz@stanford.edu
    University - Student Department: Department of Biomedical Data Science Position: Graduate

Additional Info

  • Mail Code: 3101

All Publications

  • The tidyomics ecosystem: enhancing omic data analyses. Nature methods Hutchison, W. J., Keyes, T. J., Crowell, H. L., Serizay, J., Soneson, C., Davis, E. S., Sato, N., Moses, L., Tarlinton, B., Nahid, A. A., Kosmac, M., Clayssen, Q., Yuan, V., Mu, W., Park, J. E., Mamede, I., Ryu, M. H., Axisa, P. P., Paiz, P., Poon, C. L., Tang, M., 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. 2024

    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.

    View details for DOI 10.1038/s41592-024-02299-2

    View details for PubMedID 38877315

    View details for PubMedCentralID 545600