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

Sonia Tarazona, Leandro Balzano-Nogueira, David Gómez-Cabrero, Andreas Schmidt, Axel Imhof, Thomas Hankemeier, Jesper Tegnér, Johan A. Westerhuis and Ana Conesa ()
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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
Andreas Schmidt: Protein Analysis Unit, Biomedical Center, Faculty of Medicine, LMU Munich
Axel Imhof: Protein Analysis Unit, Biomedical Center, Faculty of Medicine, 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
Johan A. Westerhuis: Swammerdam Institute for Life Sciences, University of Amsterdam
Ana Conesa: Microbiology and Cell Science Department, Institute for Food and Agricultural Research, University of Florida

Nature Communications, 2020, vol. 11, issue 1, 1-13

Abstract: 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.

Date: 2020
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DOI: 10.1038/s41467-020-16937-8

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