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HAL Id: hal-02984360

https://hal.archives-ouvertes.fr/hal-02984360 Submitted on 30 Oct 2020

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Joint automatic metabolite identification and quantification of a set of 1H NMR spectra

Gaëlle Lefort, Laurence Liaubet, Nathalie Marty-Gasset, Cécile Canlet, Nathalie Vialaneix, Rémi Servien

To cite this version:

Gaëlle Lefort, Laurence Liaubet, Nathalie Marty-Gasset, Cécile Canlet, Nathalie Vialaneix, et al.. Joint automatic metabolite identification and quantification of a set of 1H NMR spectra. Metabolomics 2020, Oct 2020, online, France. �hal-02984360�

(2)

Joint automatic metabolite identification and quantification

of a set of

1

H NMR spectra

G. Lefort

1,2

, L. Liaubet

2

, N. Marty-Gasset

2

, C. Canlet

3,4

, N. Vialaneix

1

, R. Servien

5,6

1INRAE, UR875 Math´ematiques et Informatique Appliqu´ees Toulouse, F-31326 Castanet-Tolosan, France, 2GenPhySE, Universit´e de Toulouse, INRAE, ENVT, F-31326, Castanet-Tolosan, France 3INRAE, Universit´e de Toulouse, ENVT, Toxalim, 31027 Toulouse, France,

4Axiom Platform, MetaToul-MetaboHUB, National Infrastructure for Metabolomics and Fluxomics, Toulouse, France, 5INRAE, Univ. Montpellier, LBE, 102 Avenue des ´etangs, F-11000 Narbonne, France and 6INTHERES, Universit´e de Toulouse, INRAE, ENVT, Toulouse, France

Joint automatic metabolite identification and quantification

of a set of

1

H NMR spectra

G. Lefort

1,2

, L. Liaubet

2

, N. Marty-Gasset

2

, C. Canlet

3,4

, N. Vialaneix

1

, R. Servien

5,6

1INRAE, UR875 Math´ematiques et Informatique Appliqu´ees Toulouse, F-31326 Castanet-Tolosan, France, 2GenPhySE, Universit´e de Toulouse, INRAE, ENVT, F-31326, Castanet-Tolosan, France 3INRAE, Universit´e de Toulouse, ENVT, Toxalim, 31027 Toulouse, France,

4Axiom Platform, MetaToul-MetaboHUB, National Infrastructure for Metabolomics and Fluxomics, Toulouse, France, 5INRAE, Univ. Montpellier, LBE, 102 Avenue des ´etangs, F-11000 Narbonne, France and 6INTHERES, Universit´e de Toulouse, INRAE, ENVT, Toulouse, France

From NMR spectra to metabolite quantifications

2.36

=

1.04

=

1.58

=

...

0

=

?

?

?

?

using a library of spectra

of pure compounds

metabolite quanti cations for each spectrum

Main features

Joint alignment of pure library spectra by using all complexe

mixtures together

Joint quantification and identification of metabolites using

multi-response model with a group-Lasso penalty

Independent vs. joint alignment and quantification

0 2 4 3.930 3.935 3.940 Chemical shift (ppm) In te n s it y Spectra

Complex mixture Pure library spectra (creatine) Raw spectrum Possible shift Best shift

Maximum shift

Ind

ependent

Join

t

Complex mixture Pure spectra

Metabolite quantifications

Optimization ot the FFT cross-correlation [2]

between all possible shifted pure spectra

and complex mixture with a common maximum

shift for all pure spectra

ASICS v

1

[1]

maximum

shift

Library

cleaning

Alignment of

the reference

library spectra

Metabolite

quanti cation

1

2a

2b

3

Global Local 2.36

=

=

0 1.04

=

1.58

= ...

(ppm) (model t)

4

Metabolite

selection

Join

t

Quanti cation workflow

Evaluation of the new algorithm

Data

97 samples of newborn piglet plasma on which NMR spectra and amino acid

UPLC dosages were obtained

Other tested methods

icoshift [

1

] and speaq [

2

] for alignment and rDolphin [

3

]

for quantification

ASICS

Different scenarios tested with ASICS are represented in the figure below

1

Library

cleaning

based on peak presence based on FWER selection 2

Library

alignment

independent 3 4

Metabolite

selection

with FWER control joint joint

Metabolite

quantification

joint independent

Quantification/UPLC dosage correlations

-0.5 0.0 0.5 1.0

icoshift

Alignment speaq ASICS

independent ASICS joint

Quantification FWER selection rc = 1% rc = 10% rc = 50% rDolphin

Corr

elations

ASICS independent ASICS joint

Figure 1. Correlations between metabolite quantifications from NMR spectra and UPLC dosages obtained on same samples

shown that ASICS joint alignment is better than other alignments and ASICS joint quantification is better than other

methods if it was used with a FWER procedure for metabolite selection (r

c

≥ 10%).

Conclusions

Better results with joined alignment and

quantifi-cations

Available in R ASICS

(

https://bioconductor.org/packages/ASICS/

)

Acknowledgements

[1] Lefort, G. et al. (2019). ASICS: an R package for a whole analysis workflow of 1D 1H NMR spectra. Bioinformatics, 35, 4356–4363.

[2] Wong, J. et al. (2005). Application of fast Fourier transform cross-correlation for the alignment of large chromatographic and spectral datasets. Analytical Chemistry , 77, 5655–5661.

[3] Tardivel, P. et al. (2017). Repr´esentation parcimonieuse et proc´edures de tests multiples : application `a la m´etabolomique. PhD thesis, Universi´e Toulouse 3 Paul Sabatier.

[4] Friedman, J. H. et al. (2010). Regularization paths for generalized linear models via coordinate descent. Journal of Statistical Software, 33.

[5] Yuan, M. and Lin, Y. (2006). Model selection and estimation in regression with grouped variables. Journal of the Royal Statistical Society , 68, 49–67.

[1] Savorani, F. et al. (2010). icoshift: a versatile tool for the rapid alignment of 1D NMR spectra. Journal of Magnetic Resonance, 202, 190–202.

[2] Beirnaert, C. et al. (2018). speaq 2.0: a complete workflow for high-throughput 1D NMR spectra processing and quantification. PLoS Computational Biology , 14, e1006018.

[3] Canueto, D. et al. (2018). rDolphin: a GUI R package for proficient automatic profiling of 1D 1H-NMR spectra of study datasets. Metabolomics, 14, 24.

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