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genuMet: distinguish genuine untargeted metabolic features without quality control samples

L Cao, C Clish, FB Hu, MA Martínez-González, C Razquin, M Bullo-Bonet, D Corella, E Gómez-Gracia, M Fiol, R Estruch, J Lapetra, M Fitó, F Arós, L Serra-Majem, E Ros, L Liang
doi: https://doi.org/10.1101/837260
L Cao
1Harvard TH Chan School of Public Health, Boston, MA, USA
15Carnegie Mellon University, Pittsburgh, PA, USA
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C Clish
2Broad Institute of MIT and Harvard, Cambridge, MA, USA
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FB Hu
1Harvard TH Chan School of Public Health, Boston, MA, USA
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MA Martínez-González
1Harvard TH Chan School of Public Health, Boston, MA, USA
3University of Navarra, Pamplona, Spain
4Instituto de Salud Carlos III (ISCIII), Madrid, Spain
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C Razquin
3University of Navarra, Pamplona, Spain
4Instituto de Salud Carlos III (ISCIII), Madrid, Spain
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M Bullo-Bonet
4Instituto de Salud Carlos III (ISCIII), Madrid, Spain
5Universitat Rovira i Virgili, Reus, Spain
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D Corella
4Instituto de Salud Carlos III (ISCIII), Madrid, Spain
6University of Valencia, Valencia, Spain
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E Gómez-Gracia
4Instituto de Salud Carlos III (ISCIII), Madrid, Spain
7University of Malaga, Malaga, Spain
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M Fiol
5Universitat Rovira i Virgili, Reus, Spain
8Instituto de Investigación Sanitaria de Palma, Palma de Mallorca, Spain
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R Estruch
4Instituto de Salud Carlos III (ISCIII), Madrid, Spain
9Institut d’Investigacions Biomèdiques August Pi i Sunyer, Barcelona, Spain
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J Lapetra
4Instituto de Salud Carlos III (ISCIII), Madrid, Spain
10San Pablo Health Center, Sevilla, Spain
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M Fitó
4Instituto de Salud Carlos III (ISCIII), Madrid, Spain
11Parc de Salut Mar, Barcelona, Spain
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F Arós
4Instituto de Salud Carlos III (ISCIII), Madrid, Spain
12University Hospital of Alava, Vitoria, Spain
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L Serra-Majem
4Instituto de Salud Carlos III (ISCIII), Madrid, Spain
13University of Las Palmas de Gran Canaria, Las Palmas, Spain
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E Ros
3University of Navarra, Pamplona, Spain
14Institut d’Investigacions Biomèdiques August Pi i Sunyer, Barcelona, Spain
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L Liang
1Harvard TH Chan School of Public Health, Boston, MA, USA
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  • For correspondence: lliang@hsph.harvard.edu
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Abstract

Motivation Large-scale untargeted metabolomics experiments lead to detection of thousands of novel metabolic features as well as false positive artifacts. With the incorporation of pooled QC samples and corresponding bioinformatics algorithms, those measurement artifacts can be well quality controlled. However, it is impracticable for all the studies to apply such experimental design.

Results We introduce a post-alignment quality control method called genuMet, which is solely based on injection order of biological samples to identify potential false metabolic features. In terms of the missing pattern of metabolic signals, genuMet can reach over 95% true negative rate and 85% true positive rate with suitable parameters, compared with the algorithm utilizing pooled QC samples. genu-Met makes it possible for studies without pooled QC samples to reduce false metabolic signals and perform robust statistical analysis.

Availability and implementation genuMet is implemented in a R package and available on https://github.com/liucaomics/genuMet under GPL-v2 license.

Contact Liming Liang: lliang{at}hsph.harvard.edu

Supplementary information Supplementary data are available at ….

Footnotes

  • https://github.com/liucaomics/genuMet

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
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Posted November 10, 2019.
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genuMet: distinguish genuine untargeted metabolic features without quality control samples
L Cao, C Clish, FB Hu, MA Martínez-González, C Razquin, M Bullo-Bonet, D Corella, E Gómez-Gracia, M Fiol, R Estruch, J Lapetra, M Fitó, F Arós, L Serra-Majem, E Ros, L Liang
bioRxiv 837260; doi: https://doi.org/10.1101/837260
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genuMet: distinguish genuine untargeted metabolic features without quality control samples
L Cao, C Clish, FB Hu, MA Martínez-González, C Razquin, M Bullo-Bonet, D Corella, E Gómez-Gracia, M Fiol, R Estruch, J Lapetra, M Fitó, F Arós, L Serra-Majem, E Ros, L Liang
bioRxiv 837260; doi: https://doi.org/10.1101/837260

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