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Metabolomics Data Treatment: Basic Directions of the Full Process

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Separation Techniques Applied to Omics Sciences

Part of the book series: Advances in Experimental Medicine and Biology ((PMISB,volume 1336))

Abstract

The present chapter describes basic aspects of the main steps for data processing on mass spectrometry-based metabolomics platforms, focusing on the main objectives and important considerations of each step. Initially, an overview of metabolomics and the pivotal techniques applied in the field are presented. Important features of data acquisition and preprocessing such as data compression, noise filtering, and baseline correction are revised focusing on practical aspects. Peak detection, deconvolution, and alignment as well as missing values are also discussed. Special attention is given to chemical and mathematical normalization approaches and the role of the quality control (QC) samples. Methods for uni- and multivariate statistical analysis and data pretreatment that could impact them are reviewed, emphasizing the most widely used multivariate methods, i.e., principal components analysis (PCA), partial least squares-discriminant analysis (PLS-DA), orthogonal partial least square-discriminant analysis (OPLS-DA), and hierarchical cluster analysis (HCA). Criteria for model validation and softwares used in data processing were also approached. The chapter ends with some concerns about the minimal requirements to report metadata in metabolomics.

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Abbreviations

ANN:

Artificial Neural Network

CE-MS:

Capillary Electrophoresis-Mass Spectrometry

COW:

Correlation-Optimized Warping

CV:

Coefficient of Variation

DI FT-ICR MS:

Direct-Infusion Fourier-Transform Ion-Cyclotron-Resonance Mass Spectrometry

DTW:

Dynamic Time Warping

GA:

Genetic Algorithm

GC-MS:

Gas Chromatography-Mass Spectrometry

HCA:

Hierarchical Cluster Analysis

HILIC:

Hydrophilic Interaction Chromatography

IC:

Intensity Count

kNN:

k-Nearest Neighbors

LC-MS:

Liquid Chromatography-Mass Spectrometry

LDA:

Linear Discriminant Analysis

LOESS:

Lowest Point of Smoothed Spectrum

MS:

Mass Spectrometry

NMR:

Nuclear Magnetic Resonance

NOMIS:

Normalization Using the Optimal Selection of Multiple Internal Standards

OPLS-DA:

Orthogonal Partial Least-Square Discriminant Analysis

PARAFAC:

Parallel Factor Analysis

PC:

Principal Component

PCA:

Principal Components Analysis

PLS-DA:

Partial Least Squares-Discriminant Analysis

PQN:

Probabilistic Quotient Normalization

PTW:

Parametric Time Warping

QCs:

Quality Control Samples

RAFFT:

Rapid Fast Fourier Transform

RF:

Random Forest

ROC:

Receiver Operating Characteristic Curve

ROI:

Region of Interest

S/N:

Signal-Noise Ratio

SIMCA:

Soft Independent Modeling of Class Analogy

SOM:

Self-Organization Map

SVM:

Support Vector Machine

TOF-MS:

Time of Flight-Mass Spectrometry

XIC:

Extracted Ion Chromatogram

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Authors and Affiliations

  1. Department of Analytical Chemistry, Institute of Chemistry, University of Campinas, Campinas, SP, Brazil

    Hans Rolando Zamora Obando & Gustavo Henrique Bueno Duarte

  2. Analytical Chemistry Department, University of Campinas, Institute of Chemistry, Campinas, SĂŁo Paulo, Brazil

    Ana Valéria Colnaghi Simionato

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    Ana Valéria Colnaghi Simionato

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Zamora Obando, H.R., Duarte, G.H.B., Simionato, A.V.C. (2021). Metabolomics Data Treatment: Basic Directions of the Full Process. In: Colnaghi Simionato, A.V. (eds) Separation Techniques Applied to Omics Sciences. Advances in Experimental Medicine and Biology(), vol 1336. Springer, Cham. https://doi.org/10.1007/978-3-030-77252-9_12

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