Tarazona Campos, S.; Balzano-Nogueira, L.; Gómez-Cabrero, D.; Schmidt, A.; Imhof, A.; Hankemeier, T.; Tegnér, J.... (2020). Harmonization of quality metrics and power calculation in multi-omic studies. Nature Communications. 11(1):1-13. https://doi.org/10.1038/s41467-020-16937-8
Por favor, use este identificador para citar o enlazar este ítem: http://hdl.handle.net/10251/162371
Title:
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Harmonization of quality metrics and power calculation in multi-omic studies
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Author:
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Tarazona Campos, Sonia
Balzano-Nogueira, Leandro
Gómez-Cabrero, David
Schmidt, Andreas
Imhof, Axel
Hankemeier, Thomas
Tegnér, Jesper
Westerhuis, Johan A.
Conesa, Ana
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UPV Unit:
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Universitat Politècnica de València. Departamento de Estadística e Investigación Operativa Aplicadas y Calidad - Departament d'Estadística i Investigació Operativa Aplicades i Qualitat
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Issued date:
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Abstract:
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[EN] 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 ...[+]
[EN] 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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Copyrigths:
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Reconocimiento (by)
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Source:
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Nature Communications. (issn:
2041-1723
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DOI:
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10.1038/s41467-020-16937-8
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Publisher:
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Nature Publishing Group
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Publisher version:
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https://doi.org/10.1038/s41467-020-16937-8
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Project ID:
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info:eu-repo/grantAgreement/EC/FP7/306000/EU
Ministerio de Economía y Competitividad/BIO2012-40244
DFG/SFB 1064
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Thanks:
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This work has been funded by FP7 STATegra project agreement 306000 and Spanish MINECO grant BIO2012-40244. In addition, work in the Imhof lab has been funded by the (DFG; CIPSM and SFB1064). The work of L.B.-N. has been ...[+]
This work has been funded by FP7 STATegra project agreement 306000 and Spanish MINECO grant BIO2012-40244. In addition, work in the Imhof lab has been funded by the (DFG; CIPSM and SFB1064). The work of L.B.-N. has been funded by the University of Florida Startup funds.
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Type:
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Artículo
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