# NMR in Metabolomics: From Conventional Statistics to Machine Learning and Neural Network Approaches

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## Abstract

**:**

## 1. Introduction

## 2. Conventional Approaches

#### 2.1. Unsupervised Methods

#### 2.1.1. Principal Component Analysis (PCA)

#### 2.1.2. Clustering

#### 2.1.3. Self-Organizing Maps (SOMs)

#### 2.2. Supervised Methods

- The filter method marks subgroups of variables by calculate “easy to compute” quantities ahead of the model training.
- The wrapper method marks subgroups of variables by applying the chosen trained models on the testing dataset with the aim to determine the achieving the optimal performance.
- The embedded method is able to ascertain simultaneously the feature selection and model structure.

#### 2.2.1. Random Forest (RF) and k-Nearest Neighbors (KNN)

_{i}and with the aim to predict Y, the KNN algorithm selects the k-nearest observations of X

_{i}in ${\{{Y}_{j},{X}_{j}\}}_{1jK}$. Let ${i}_{1},...,{i}_{k}$ be the k values which provide the k minimum values of the function: g(j) = d(X${}_{j}$ - X${}_{i}$). These minimum values can be equal if there are multiple values of X${}_{j}$ at the same distance from X${}_{i}$ [59]. There are at least the three possibilities for the distance (Euclidean, Manhattan and Minkowski). So, the value predicted for Y${}_{i}$ is the mean value of the k values Y${}_{j}$ for the k nearest neighbors of X${}_{i}$:

#### 2.2.2. Principal Component Regression (PCR) and Partial Least Squares (PLS)

#### 2.2.3. Support Vector Machine (SVM)

#### 2.3. Pathway Analysis Methods

#### 2.3.1. Over-Representation Analysis (ORA)

#### 2.3.2. Functional Class Scoring (FCS)

- A statistical approach is applied to compute differential expression of individual metabolites (metabolite-level statistics), looking for correlations of molecular measurements with phenotype [87]. Those mostly used consider the analysis of variance (ANOVA) [88], Q-statistic [89], signal-to-noise ratio [90], t-test [91], and Z-score [92]. The choice of the most suitable statistical approach may depend on the number of biological replicates and on the effect of the metabolites set on a specific pathway [93].
- Initial statistics for all metabolites of a given pathway are combined into statistics on different pathways (pathway-level statistics) that can consider interdependencies among metabolites (multivariate) [94] or not (univariate) [91]. The pathway-level statistics usually is performed in terms of the Kolmogorov–Smirnov statistics [90], mean or median of metabolite-level statistics [93], the Wilcoxon rank sum [95], and the maxmean statistics [96]. Note that, although multivariate statistics should have more statistical significance, univariate statistics provide the best results if applied to the data of biologic systems (p≤ 0.001) [97].
- The last FCS step corresponds to estimating the significance of the so-called pathway-level statistics. In detail, the null hypothesis can be tested into two different ways: (i) by permuting metabolite labels for every pathways, so comparing the set of metabolites in that pathway with a set of metabolites not included in that pathway (competitive null hypothesis) [75] and (ii) by permuting class labels for every sample, so comparing the collection of metabolites in a considered pathway with itself, whereas the metabolites excluded by that pathway are not considered (self-contained null hypothesis) [91].

#### 2.3.3. Metabolic Pathway Reconstruction and Simulation

## 3. Artificial Intelligence toward Learning Techniques

#### Machine Learning, Neural Networks and Deep Learning

- Supervised learning (discriminative) includes multi-layer perceptron (MLP), convolutional neural network (CNN), long short-term memory (LSTM) and gated recurrent unit (GRU);
- Unsupervised learning (generative) includes generative adversarial network (GAN), autoencoder (AE), sparse autoencoder (SAE), denoising autoencoder (DAE), contractive autoencoder (CAE), variational autoencoder (VAE), self-organizing map (SOM), restricted Boltzmann machine (RBM) and deep belief network (DBN);
- Hybrid learning (both discriminative and generative) includes models composed by both supervised and unsupervised algorithms other than deep transfer learning (DTL) and deep reinforcement learning (DRL).

- 1.
**Classic neural networks**encompass linear and non-linear functions which, in turn, include S-shaped functions ranging from 0 to 1 (sigmoid) or from −1 to 1 (hyperbolic tangent, tanh) and rectified linear unit (ReLU), which gives 0 for input lower than the set value or evaluates a linear multiple for bigger input.- 2.
**Convolutional neural networks (CNN)**take into high consideration the neuron organization found in the visual cortex of an animal brain. It is particularly suited for high complexity and allows for optimal pre-processing. Four stages can be considered for CNN building (see Figure 17):- (a)
- Deduce feature maps from input after applying a proper function (convolution);
- (b)
- Reveal an image after given changes (max-pooling);
- (c)
- Flatten the data for the CNN analysis (flattening);
- (d)
- Compiling the loss function by a hidden layer (full connection).

- 3.
**Recurrent neural networks (RNN)**are exploited when the objective is the prediction of a sequence. They are a subset of ANN for sequential or time series data, usually applied for language translation, speech recognition, and son on. Their peculiar feature is that the outcome of the output node is a function of the output of previous elements within the sequence (see Figure 18a).- 4.
**Generative adversarial networks (GAN)**combine generator networks for providing artificial data and discriminator networks for distinguishing real and fake data.- 5.
**Self-organizing maps (SOMs)**have a fixed bi-dimensional output since each synapse joins its input and output nodes, and usually take advantage of data reduction performed by unsupervised approaches.- 6.
**Boltzmann machine**is a stochastic model exploited for yielding proper parameters defined in the model.- 7.
**Deep reinforcement learning**are mainly used to understand and so predict the effect of every action executed in a defined state of the observation.- 8.
**Autoencoders**work directly on the considered inputs, without taking into account the effect of activation functions. Among the autoencoders, we mention the following:- (a)
- Sparse autoencoders have more hidden than input layers for reducing overfitting.
- (b)
- Denoising autoencoders are able to reconstruct corrupted data by randomly assigning 0 to some inputs.
- (c)
- Contractive autoencoders include a penalty factor to the loss function to prevent overfitting and data repetition when the network has more hidden than input layers.
- (d)
- Stacked autoencoders perform two stages of encoding by the inclusion of an additional hidden layer.

- 9.
**Backpropagation (BP)**are neural networks that use the flux of information going from the output to input for learning about the errors corresponding to the achieved prediction. An architecture of the BP network is shown in Figure 18b.- 10.
**Gradient descent**are neural networks that identify a slope corresponding to a relation among variables (for example, the error produced in the neural network and data parameter: small data changes provoke errors variations).

## 4. Applications of Deep Learning Approaches for NMR-Based Metabolomics

#### 4.1. Food

#### 4.2. Biomedical

## 5. Conclusions and Future Perspective

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Conflicts of Interest

## Abbreviations

NMR | Nuclear Magnetic Resonance |

MS | Mass Spectrometry |

AI | Artificial Intelligence |

ML | Machine Learning |

DL | Deep Learning |

NN | Neural Network |

ANN | Artificial Neural Network |

DNN | Deep Neural Network |

PCA | Principal Component Analysis |

PLS | Partial Least Squares |

ORA | Over Representation Analysis |

FCS | Functional Class Scoring |

## Appendix A. Technical Aspects

**Figure A1.**(

**A**) Spectroscopies and corresponding frequency ranges. Larmor frequency of most used nuclei for metabolomics analyses with respect to that of the proton when at 600 MHz. (

**B**) Parts per million intervals for all these nuclei (${}^{15}$N, ${}^{13}$C, ${}^{31}$P, ${}^{19}$F and ${}^{1}$H) at characteristic chemical environments. Figure reprinted from Ref. [3] under the terms of the Creative Common CC-BY license.

**Figure A2.**${}^{1}$H NMR spectrum of ultrafiltered human serum at 700 MHz with the identified compounds labeled above each of the corresponding peaks. Figure reprinted from Ref. [3] under the terms of the Creative Common CC-BY license.

**Figure A3.**${}^{1}$H-${}^{13}$C 2D HSQC experiment to identify TMAO and betaine organic compounds of a biological matrix. Figure reprinted from Ref. [145] under the terms of the Creative Common CC-BY license.

**Figure A4.**(

**A**): The spiking of UDP-nacetylglucosamine (UDP-Gluc-NAc) allows its identification and quantitation. (

**B**): The same spectrum of A without the addition of UDP-Gluc-NAc. Figure reprinted from Ref. [145] under the terms of the Creative Common CC-BY license.

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**Figure 1.**Schematic workflow illustrating the steps of NMR based metabolomic studies coupled with chemometrics and pathway analysis. (1) Sample preparation and NMR tube filling (top left); (2) experimental parameters setting and data acquisition (top right); (3) data processing (middle left); (4) execution of multivariate statistical analysis (bottom right); (5) determination of metabolic pathways (bottom left). Some figures are reprinted from Refs. [22,23] under the terms of the CC-BY license.

**Figure 2.**Example plot with 3 variable axes in a n-dimensional variable space. The principal components PC1 and PC2 are reported.

**Figure 3.**An example of a dendrogram obtained by means of hierarchical cluster analysis performed on ${}^{1}$H NMR data on the plasma metabolome of 50 patients with early breast cancer. From the analysis, 3 different groups are classified: LR-1 (red), LR-2 (blue) and LR-3 (green). In this case, the Ward algorithm is adopted for measuring the distance. Figure reprinted from Ref. [48] under the terms of the CC-BY license.

**Figure 4.**An example of SOM model for studying renal cell carcinoma (RCC). (

**A**) SOM classification and discrimination between healthy subjects (left region) and RCC patients (right region) by considering 16 metabolites extracted by means of NMR spectroscopy on serum samples. (

**B**–

**Q**) Weight maps of the considered 16 metabolites. Darker colors correspond to higher SOM weights. Figure reprinted from Ref. [52] under the terms of the CC-BY license.

**Figure 5.**Scheme about merits and demerits of supervised methods, including filter, wrapper and embedded feature selection approaches.

**Figure 6.**ROC curves and corresponding AUC values for three classifiers: no predicting power (red dashed line with AUC = 0.5), perfect classifier (green dotted line with AUC = 1) and some predictive power (blue solid line with AUC∼0.8).

**Figure 7.**Example of decision tree with a different action corresponding to a different conditions set.

**Figure 8.**(

**a**) Bidimensional PLS-DA score plot of urine samples obtained from different hospitals. HB—Basurto Hospital, CRC—Cruces Hospital, HD—Donosti Hospital, TX—Txagorritxu Hospital. Figure reprinted from [67] under the terms of Creative Commons Attribution 4.0 International License. (

**b**) OPLS scheme.

**Figure 9.**Linear SVM model highlighting the classification of two classes (red and blue). Figure reprinted from Ref. [68] under the terms of the HighWire Press license.

**Figure 11.**A 3D Venn diagram illustrating the relation between ORA parameters (Equation (6)) in which N corresponds to the number of background compounds, n is the number of the measured metabolites, M is the number of background metabolites mapping the ith pathway, and k represents the overlap between M and n.

**Figure 13.**Pathway analysis performed on serum spectra recorded by ${}^{1}$H NMR allowing the identification of main metabolic pathways associated with non-small cell lung cancer. The larger the circle, the higher the impact. The color, from red to yellow, identifies the corresponding significance. Figure reprinted from [23] under the terms of the Creative Commons Attribution 4.0 International License.

**Figure 14.**Venn diagram illustrating that deep learning is the core of machine learning, which in turn is a technique within AI methods.

**Figure 15.**(

**a**) Example scheme of a deep neural networks, reprinted from Ref. [103] under the terms of the CC-BY license; (

**b**) operating principle of a single node.

**Figure 17.**Example of a convolutional neural network. Figure reprinted from Ref. [109] under the terms of CC BY-NC-ND 4.0 license.

**Figure 18.**(

**a**) Scheme of a RNN. (

**b**) Example of a BP network architecture. Figure reprinted from Ref. [109] under the terms of CC BY-NC-ND 4.0 license.

**Figure 19.**T${}_{1}$–T${}_{2}$ correlational maps classifying several kinds of oils: olive (blue), canola (orange), corn (yellow) and vegetable (purple) by using the two components used by the Gaussian fit of those peaks revealed by the inverse 2D Laplace transform. (

**a**,

**c**) report the first component of T${}_{1}$ versus the first and second components of T${}_{2}$, respectively. (

**b**,

**d**) report the second component of T${}_{1}$ versus the first and second components of T${}_{2}$, respectively. See main text and Ref. [116] for details. Figure reprinted with permission from Ref. [116]. Copyright 2018 Elsevier.

**Figure 20.**(

**a**) Comparison of the accuracy for the predictive power of the algorithms applied to classify cooking oil samples by employing three different classification training; (

**b**) accuracy of predictive power applied to soy sauce sample highlighting the effect of temperature. Figure adapted with permission from Ref. [116]. Copyright 2018 Elsevier.

**Figure 21.**Multilayer artificial neural network showing 4 input neurons, 2 hidden layers made of 3 neurons, and 2 output neurons of which the one corresponding to “Salmonella” shows the highest value, associated with the prediction performed by the used ANN. Figure reprinted from Ref. [119] under the terms of the CC-BY license.

**Figure 22.**Comparison of the different criteria adopted for the ANN training. (

**a**) “Greedy” learning; (

**b**) “jumping” out of a local tiny minimum; (

**c**) halt at large minima; (

**d**) halt at sharp growths in loss. Figure reprinted from Ref. [119] under the terms of the CC-BY license.

**Figure 23.**The multiomics method represented connects biological (i.e., signal inhibition, signaling network and biochemical feedback) with DL modeling (backpropagation, prediction, convolution, etc.), aiming to maximize the robustness of the approach for the identification of biochemical features referred to specific phenotypes. Figure reprinted from Ref. [124] under the terms of the Creative Commons Attribution Noncommercial License.

**Figure 24.**Overlay of experimental HSQC spectra from a metabolite mixture (black correlations) and the outcomes predicted by SMART-Miner (colored correlations). Figure reprinted with permission from Ref. [126]. Copyright 2021 Wiley Periodicals, Inc.

**Figure 25.**(

**A**) Example of ${}^{1}$H NMR spectrum for DLKP lung carcinoma cells. Labeled peak corresponds to (a) CH${}_{3}$, (b) CH${}_{2}$, (c) CH${}_{2}$CH=CH, (d) CH${}_{2}$COO, (e) =CHCH${}_{2}$CH=, and (f) HC=CH/CHOCOR. The highlighted intervals at 0.60–1.04 and 1.24–3.56 ppm were used for statistical analysis. (

**B**) PCA score plot including data from all four cell lines. (

**C**) Residual mean squares error vs. nodes number in the hidden layers, for the 3-layers (full symbols), and in the second (empty triangles) and third (empty circles) layer for the 4-layers networks. Figure reprinted from Ref. [127] under the terms of the Creative Commons Attribution License.

**Figure 26.**T${}_{1}$–T${}_{2}$ correlational maps in false colors of red blood cells at different conditions: oxygenated (

**a**), oxidized (

**b**), and deoxygenated (

**c**). Figure reprinted from Ref. [130] under the terms of the Creative Commons Attribution 4.0 International License.

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Corsaro, C.; Vasi, S.; Neri, F.; Mezzasalma, A.M.; Neri, G.; Fazio, E.
NMR in Metabolomics: From Conventional Statistics to Machine Learning and Neural Network Approaches. *Appl. Sci.* **2022**, *12*, 2824.
https://doi.org/10.3390/app12062824

**AMA Style**

Corsaro C, Vasi S, Neri F, Mezzasalma AM, Neri G, Fazio E.
NMR in Metabolomics: From Conventional Statistics to Machine Learning and Neural Network Approaches. *Applied Sciences*. 2022; 12(6):2824.
https://doi.org/10.3390/app12062824

**Chicago/Turabian Style**

Corsaro, Carmelo, Sebastiano Vasi, Fortunato Neri, Angela Maria Mezzasalma, Giulia Neri, and Enza Fazio.
2022. "NMR in Metabolomics: From Conventional Statistics to Machine Learning and Neural Network Approaches" *Applied Sciences* 12, no. 6: 2824.
https://doi.org/10.3390/app12062824