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Cited by 105Bioinformatics and Genomic NetworksGene expression and cancer classificationOptimal Experimental Design Methods

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

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Tarazona, Sonia, Balzano-Nogueira, Leandro, Gómez-Cabrero, David, Schmidt, Andreas, Imhof, Axel, Hankemeier, Thomas, Tegnér, Jesper, Westerhuis, Johan A. +1 more·2020·Nature Communications
DOI 10.1038/s41467-020-16937-8PMC PMC7303201
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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.

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  • MultiPower modeling has shown that approximately 500 paired omics and imaging samples are required to detect moderate molecular effects with a 5% false discovery rate in integrative analyses.

    “For instance, MultiPower modeling has shown that approximately 500 paired omics and imaging samples are required to detect moderate molecular effects with a 5% false discovery rate in integrative analyses407.”

    From Artificial intelligence-driven multi-omics approaches in Alzheimer's disease: Progress, challenges, and future directions (2025) · cites this paper as [407]

    The Results section reports MultiPower sample-size modeling with an FDR of 0.05 and an initial Cohen’s d of 0.8, but estimates 16 replicates for the STATegra multi-omics example. It also estimates n = 24 for a complete-design glioblastoma multi-omics analysis. Neither example supports approximately 500 paired omics and imaging samples; the reported analyses do not include imaging. (LLM, conf=0.90)

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Authors
Tarazona, S., Balzano-Nogueira, L., Gómez-Cabrero, D., Schmidt, A., Imhof, A., Hankemeier, T., Tegnér, J., Westerhuis, J. A., & Conesa, A.
Affiliations
  • Universitat Politècnica de València (ES)
  • Universidad Pública de Navarra (UPNA) (ES)
  • Ludwig-Maximilians-Universität München (DE)
  • Center for Integrated Protein Science Munich (DE)
  • North-West University (ZA)
  • University of Florida (US)
Corresponding author
Conesa, Ana
ORCID
Venue
Nature Communications
Publisher
Springer Science and Business Media LLC
Published
2020-06-18
Volume
11
Issue
1
Pages
3092-3092
ISSN
2041-1723 (ISSN-L)
DOI
10.1038/s41467-020-16937-8
PMC
PMC7303201
OpenAlex
W3036926163
Cited by
105
References
8
Citations by year
2026: 15 · 2025: 26 · 2024: 22 · 2023: 19 · 2022: 14 · 2021: 7 · 2020: 1
FWCI
3.33
Type
journal article
Language
en
Open access
Licence
Locations
  • Nature Communications (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • UvA-DARE (University of Amsterdam) (submittedVersion, cc-by)
  • RePEc: Research Papers in Economics (submittedVersion)
  • Leiden Repository (Leiden University) (submittedVersion, cc-by)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
  • Open access LMU (Ludwid Maxmilian's Universitat Munchen) (submittedVersion, cc-by)
  • Leiden Repository (Leiden University) (submittedVersion)
  • King Abdullah University of Science and Technology Repository (King Abdullah University of Science and Technology) (submittedVersion, other-oa)
  • Boloka Institutional Repository (North-west University) (submittedVersion, cc-by)
  • RiuNet (Universitat Politècnica de València) (submittedVersion, cc-by-nc-nd)
  • Data Archiving and Networked Services (DANS) (submittedVersion, cc-by)
  • Data Archiving and Networked Services (DANS) (submittedVersion, other-oa)
Funders
  • EC | EC Seventh Framework Programm | FP7 Health (306000)
  • Ministerio de Economía, Industria y Competitividad, Gobierno de España (BIO2012-40244)
  • Deutsche Forschungsgemeinschaft (SFB1064)
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
  • Social Sciences › Decision Sciences › Management Science and Operations Research › Optimal Experimental Design Methods
Keywords
multi-omics, sample size determination, power analysis, figure of merit, experimental design, machine learning classification
MeSH terms
Machine Learning, Algorithms, Quality Control, Computational Biology

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