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  • Brief Communication
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The tidyomics ecosystem: enhancing omic data analyses


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.

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Fig. 1: Overview of the tidyomics ecosystem.
Fig. 2: Performance of the tidyomics ecosystem.

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Data availability

Human Cell Atlas peripheral blood mononuclear single-cell data were downloaded from the CELLxGENE database. The relative weblink for each sample is listed in Supplementary Table 1. The samples analyzed are accessible at the Human Cell Atlas. Metadata and gene-transcript abundance for these datasets from the CuratedAtlasQuery database is accessible at sample_metadata.0.2.3.parquet. CELLxGENE sample accession codes are available in Supplementary Table 1. Source data are provided with this paper.

Code availability

The tidyomics homepage is, which provides links to the constituent packages. The tidyomics meta-package is available at Bioconductor The tidySummarizedExperiment package is available at Bioconductor The tidySingleCellExperiment package is available at Bioconductor The tidySpatialExperiment package is available at Bioconductor The code used to benchmark workflow efficiency and analyze peripheral blood mononuclear cells from the Human Cell Atlas is available at Source data for Fig. 2h are available at


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We acknowledge Bioconductor and tidyverse communities, whose software and coding paradigms this work is based on and would not be possible without. We also thank the tidyomics community for their feedback and contribution. We thank V. Carey for his support and feedback on the project. Also, we thank M. Ritchie for his continuous support and feedback. Human illustrations were created with S.M. was supported by the Victorian Cancer Agency Early Career Research Fellowship (ECRF21036). M.I.L. was supported by the Chan Zuckerberg Initiative (EOSS3-0000000057). A.T.P. was supported by the National Health and Medical Research Council (NHMRC) Senior Research Fellowship (1116955) and Investigator Grant (2026643). A.T.P., S.M. and W.H. were supported by the Lorenzo and Pamela Galli Medical Research Trust and the Galli Next Generation Discoveries Initiative. K.L.D. is the Anne T. and Robert M. Bass Endowed Faculty Scholar in Pediatric Cancer and Blood Diseases of the Stanford Maternal Child Health Research Institute and the Harriet and Mary Zelencik Endowed Faculty in Children’s Cancer and Blood Diseases. P.-P.A. was supported by the Cancéropole GSO and Intergroupe Français du Myélome. R.G. was funded by a project grant from the Swiss National Foundation. M.M. was supported by the NHGRI and NCI of the National Institutes of Health under award numbers U41HG004059 and U24CA180996. This work was supported by an ASPIRE award from the Mark Foundation for Cancer Research and the B+ Foundation. The research benefited from support from the Victorian State Government Operational Infrastructure Support and Australian Government NHMRC Independent Research Institute Infrastructure Support. The funders had no role in study design, data collection and analysis, or decision to publish or prepare the manuscript.

Author information

Authors and Affiliations




S.M. proposed the study, and S.M. and M.I.L. designed the study. W.J.H. and S.M. developed the novel tidy adapters for transcriptomics, W.J.H., T.J.K., S.M. and M.I.L. performed the analyses. W.J.H., T.J.K., H.L.C., J.S., C.S., E.S.D., N.S., L.M., B.T., A.A.N., M.K., Q.C., V.Y., W.M., J.-E.P., I.M., M.H.R., P.-P.A., P.P., C.-L.P., M.T., R.G., M.M., S.L., M.L., S.C.H., G.P.N., K.L.D., A.T.P., M.I.L. and S.M. contributed to the ecosystem’s development and ongoing improvement. S.M., M.I.L., A.T.P., K.L.D., S.C.H., M.L., M.M. and R.G. acted as the supervisory team. S.M., M.I.L. and A.T.P. contributed equally and jointly led the study. W.J.H. and T.J.K. contributed equally. All authors contributed to the manuscript’s writing.

Corresponding authors

Correspondence to Anthony T. Papenfuss, Michael I. Love or Stefano Mangiola.

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Competing interests

R.G. has received consulting income from Takeda and Sanofi, and declares ownership in Ozette Technologies. M.K. is an employee of and declares ownership in Achilles Therapeutics. The other authors declare no competing interests.

Peer review

Peer review information

Nature Methods thanks Bo Li and Judith Zaugg for their contribution to the peer review of this work. Primary Handling Editor: Lei Tang, in collaboration with the Nature Methods team.

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Supplementary information

Reporting Summary

Supplementary Table 1

List of samples used in peripheral blood mononuclear cell analysis.

Source data

Source Data Fig. 2

Source data used to create the benchmarking plot Fig. 2h.

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Hutchison, W.J., Keyes, T.J., The tidyomics Consortium. et al. The tidyomics ecosystem: enhancing omic data analyses. Nat Methods 21, 1166–1170 (2024).

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