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Harmonization of quality metrics and power calculation in multi-omic studies

Author

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  • Sonia Tarazona

    (Department of Applied Statistics, Operations Research and Quality, Universitat Politècnica de València)

  • Leandro Balzano-Nogueira

    (Microbiology and Cell Science Department, Institute for Food and Agricultural Research, University of Florida)

  • David Gómez-Cabrero

    (Unit of Computational Medicine, Department of Medicine, Solna, Center for Molecular Medicine, Karolinska Institutet
    Science for Life Laboratory
    Mucosal & Salivary Biology Division, King’s College London Dental Institute
    Navarrabiomed, Complejo Hospitalario de Navarra (CHN), Universidad Pública de Navarra (UPNA), IdiSNA)

  • Andreas Schmidt

    (Protein Analysis Unit, Biomedical Center, Faculty of Medicine, LMU Munich)

  • Axel Imhof

    (Protein Analysis Unit, Biomedical Center, Faculty of Medicine, LMU Munich
    Munich Center of Integrated Protein Science LMU Munich)

  • Thomas Hankemeier

    (Division Analytical Biosciences, Leiden/Amsterdam Center for Drug Research)

  • Jesper Tegnér

    (Unit of Computational Medicine, Department of Medicine, Solna, Center for Molecular Medicine, Karolinska Institutet
    Science for Life Laboratory
    Biological and Environmental Sciences and Engineering Division, Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology)

  • Johan A. Westerhuis

    (Swammerdam Institute for Life Sciences, University of Amsterdam
    Department of Statistics, Faculty of Natural Sciences, North-West University (Potchefstroom Campus))

  • Ana Conesa

    (Microbiology and Cell Science Department, Institute for Food and Agricultural Research, University of Florida
    Genetics Institute, University of Florida)

Abstract

Multi-omic studies combine measurements at different molecular levels to build comprehensive models of cellular systems. The success of a multi-omic data analysis strategy depends largely on the adoption of adequate experimental designs, and on the quality of the measurements provided by the different omic platforms. However, the field lacks a comparative description of performance parameters across omic technologies and a formulation for experimental design in multi-omic data scenarios. Here, we propose a set of harmonized Figures of Merit (FoM) as quality descriptors applicable to different omic data types. Employing this information, we formulate the MultiPower method to estimate and assess the optimal sample size in a multi-omics experiment. MultiPower supports different experimental settings, data types and sample sizes, and includes graphical for experimental design decision-making. MultiPower is complemented with MultiML, an algorithm to estimate sample size for machine learning classification problems based on multi-omic data.

Suggested Citation

  • Sonia Tarazona & Leandro Balzano-Nogueira & David Gómez-Cabrero & Andreas Schmidt & Axel Imhof & Thomas Hankemeier & Jesper Tegnér & Johan A. Westerhuis & Ana Conesa, 2020. "Harmonization of quality metrics and power calculation in multi-omic studies," Nature Communications, Nature, vol. 11(1), pages 1-13, December.
    • Handle: RePEc:nat:natcom:v:11:y:2020:i:1:d:10.1038_s41467-020-16937-8
    DOI: 10.1038/s41467-020-16937-8
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    Cited by:

    1. Ramon Diaz-Uriarte & Elisa Gómez de Lope & Rosalba Giugno & Holger Fröhlich & Petr V Nazarov & Isabel A Nepomuceno-Chamorro & Armin Rauschenberger & Enrico Glaab, 2022. "Ten quick tips for biomarker discovery and validation analyses using machine learning," PLOS Computational Biology, Public Library of Science, vol. 18(8), pages 1-17, August.
    2. Yan Zhao & Changchun Ma & Rongzhi Cai & Lijing Xin & Yongsheng Li & Lixin Ke & Wei Ye & Ting Ouyang & Jiahao Liang & Renhua Wu & Yan Lin, 2024. "NMR and MS reveal characteristic metabolome atlas and optimize esophageal squamous cell carcinoma early detection," Nature Communications, Nature, vol. 15(1), pages 1-17, December.
    3. Rafal S. Sobota & Emily M. Stucke & Drissa Coulibaly & Jonathan G. Lawton & Bryan E. Cummings & Savy Sebastian & Antoine Dara & James B. Munro & Amed Ouattara & Abdoulaye K. Kone & Bourama Kane & Kari, 2025. "A shared inflammatory signature across severe malaria syndromes manifested by transcriptomic, proteomic and metabolomic analyses," Nature Communications, Nature, vol. 16(1), pages 1-14, December.
    4. Anna Luiza Silva Almeida Vicente & Alexei Novoloaca & Vincent Cahais & Zainab Awada & Cyrille Cuenin & Natália Spitz & André Lopes Carvalho & Adriane Feijó Evangelista & Camila Souza Crovador & Rui Ma, 2022. "Cutaneous and acral melanoma cross-OMICs reveals prognostic cancer drivers associated with pathobiology and ultraviolet exposure," Nature Communications, Nature, vol. 13(1), pages 1-15, December.

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