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Zamora Obando HR, Duarte GHB, Simionato AVC. Metabolomics Data Treatment: Basic Directions of the Full Process. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2021; 1336:243-264. [PMID: 34628635 DOI: 10.1007/978-3-030-77252-9_12] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/10/2023]
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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Affiliation(s)
- Hans Rolando Zamora Obando
- Department of Analytical Chemistry, Institute of Chemistry, University of Campinas, Campinas, SP, Brazil
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Pomyen Y, Wanichthanarak K, Poungsombat P, Fahrmann J, Grapov D, Khoomrung S. Deep metabolome: Applications of deep learning in metabolomics. Comput Struct Biotechnol J 2020; 18:2818-2825. [PMID: 33133423 PMCID: PMC7575644 DOI: 10.1016/j.csbj.2020.09.033] [Citation(s) in RCA: 67] [Impact Index Per Article: 16.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2020] [Revised: 09/21/2020] [Accepted: 09/21/2020] [Indexed: 01/11/2023] Open
Abstract
In the past few years, deep learning has been successfully applied to various omics data. However, the applications of deep learning in metabolomics are still relatively low compared to others omics. Currently, data pre-processing using convolutional neural network architecture appears to benefit the most from deep learning. Compound/structure identification and quantification using artificial neural network/deep learning performed relatively better than traditional machine learning techniques, whereas only marginally better results are observed in biological interpretations. Before deep learning can be effectively applied to metabolomics, several challenges should be addressed, including metabolome-specific deep learning architectures, dimensionality problems, and model evaluation regimes.
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Key Words
- AI, Artificial Intelligence
- ANN, Artificial Neural Network
- AUC, Area Under the receiver-operating characteristic Curve
- Artificial neural network
- CCS value, Collision Cross Section value
- CFM-EI, Competitive Fragmentation Modeling-Electron Ionization
- CNN, Convolutional Neural Network
- DL, Deep Learning
- DNN, Deep Neural Network
- Deep learning
- ECFP, Extended Circular Fingerprint
- ER, Estrogen Receptor
- FID, Free Induction Decay
- FP score, Fingerprint correlation score
- FTIR, Fourier Transform Infrared
- GC–MS, Gas Chromatography-Mass Spectrometry
- HDLSS data, High Dimensional Low Sample Size data
- IST, Iterative Soft Thresholding
- LC-MS, Liquid Chromatography-Mass Spectrometry
- LSTM, Long Short-Term Memory
- ML, Machine Learning
- MLP, Multi-layered Perceptron
- MS, Mass Spectrometry
- Mass spectrometry
- Metabolomics
- NEIMS, Neural Electron-Ionization Mass Spectrometry
- NMR
- NMR, Nuclear Magnetic Resonance
- NUS, Non-Uniformly Sampling
- PARAFAC2, Parallel Factor Analysis 2
- RF, Random Forest
- RNN, Recurrent Neural Network
- ReLU, Rectified Linear Unit
- SMARTS, SMILES arbitrary target specification
- SMILE, Sparse Multidimensional Iterative Lineshape-enhanced
- SMILES, Simplified Molecular-Input Line-Entry System
- SRA, Sequence Read Archive
- VAE, Variational Autoencoder
- istHMS, Implementation of IST at Harvard Medical School
- m/z, mass/charge ratio
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Affiliation(s)
- Yotsawat Pomyen
- Translational Research Unit, Chulabhorn Research Institute, Bangkok, Thailand
| | - Kwanjeera Wanichthanarak
- Metabolomics and Systems Biology, Department of Biochemistry, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok 10700, Thailand
- Siriraj Metabolomics and Phenomics Center, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok 10700, Thailand
| | - Patcha Poungsombat
- Metabolomics and Systems Biology, Department of Biochemistry, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok 10700, Thailand
- Siriraj Metabolomics and Phenomics Center, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok 10700, Thailand
- Center for Innovation in Chemistry (PERCH-CIC), Faculty of Science, Mahidol University, Rama 6 Road, Bangkok 10400, Thailand
| | - Johannes Fahrmann
- Department of Clinical Cancer Prevention, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Boulevard, Houston, TX 77030, USA
| | - Dmitry Grapov
- CDS- Creative Data Solutions LLC, https://creative-data.solutions, USA
| | - Sakda Khoomrung
- Metabolomics and Systems Biology, Department of Biochemistry, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok 10700, Thailand
- Siriraj Metabolomics and Phenomics Center, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok 10700, Thailand
- Center for Innovation in Chemistry (PERCH-CIC), Faculty of Science, Mahidol University, Rama 6 Road, Bangkok 10400, Thailand
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