TY  - GEN
AU  - Hanhineva,Kati
AU  - Van der Hooft,Justin
AU  - Hanhineva,Kati
AU  - Van der Hooft,Justin
TI  - Metabolomics Data Processing and Data Analysis-Current Best Practices
SN  - books978-3-0365-1195-5
PY  - 2021///
CY  - Basel, Switzerland
PB  - MDPI - Multidisciplinary Digital Publishing Institute
KW  - Research & information: general
KW  - bicssc
KW  - metabolic networks
KW  - mass spectral libraries
KW  - metabolite annotation
KW  - metabolomics data mapping
KW  - nontarget analysis
KW  - liquid chromatography mass spectrometry
KW  - compound identification
KW  - tandem mass spectral library
KW  - forensics
KW  - wastewater
KW  - gut microbiome
KW  - meta-omics
KW  - metagenomics
KW  - metabolomics
KW  - metabolic reconstructions
KW  - genome-scale metabolic modeling
KW  - constraint-based modeling
KW  - flux balance
KW  - host-microbiome
KW  - metabolism
KW  - global metabolomics
KW  - LC-MS
KW  - spectra processing
KW  - pathway analysis
KW  - enrichment analysis
KW  - mass spectrometry
KW  - liquid chromatography
KW  - MS spectral prediction
KW  - metabolite identification
KW  - structure-based chemical classification
KW  - rule-based fragmentation
KW  - combinatorial fragmentation
KW  - time series
KW  - PLS
KW  - NPLS
KW  - variable selection
KW  - bootstrapped-VIP
KW  - data repository
KW  - computational metabolomics
KW  - reanalysis
KW  - lipidomics
KW  - data processing
KW  - triplot
KW  - multivariate risk modeling
KW  - environmental factors
KW  - disease risk
KW  - chemical classification
KW  - in silico workflows
KW  - metabolome mining
KW  - molecular families
KW  - networking
KW  - substructures
KW  - mass spectrometry imaging
KW  - metabolomics imaging
KW  - biostatistics
KW  - ion selection algorithms
KW  - liquid chromatography high-resolution mass spectrometry
KW  - data-independent acquisition
KW  - all ion fragmentation
KW  - targeted analysis
KW  - untargeted analysis
KW  - R programming
KW  - full-scan MS/MS processing
KW  - R-MetaboList 2
KW  - liquid chromatography-mass spectrometry (LC/MS)
KW  - fragmentation (MS/MS)
KW  - data-dependent acquisition (DDA)
KW  - simulator
KW  - in silico
KW  - untargeted metabolomics
KW  - liquid chromatography-mass spectrometry (LC-MS)
KW  - experimental design
KW  - sample preparation
KW  - univariate and multivariate statistics
KW  - metabolic pathway and network analysis
KW  - metabolic profiling
KW  - computational statistical
KW  - unsupervised learning
KW  - supervised learning
N1  - Open Access
N2  - Metabolomics data analysis strategies are central to transforming raw metabolomics data files into meaningful biochemical interpretations that answer biological questions or generate novel hypotheses. This book contains a variety of papers from a Special Issue around the theme "Best Practices in Metabolomics Data Analysis". Reviews and strategies for the whole metabolomics pipeline are included, whereas key areas such as metabolite annotation and identification, compound and spectral databases and repositories, and statistical analysis are highlighted in various papers. Altogether, this book contains valuable information for researchers just starting in their metabolomics career as well as those that are more experienced and look for additional knowledge and best practice to complement key parts of their metabolomics workflows
UR  - https://mdpi.com/books/pdfview/book/4323
UR  - https://directory.doabooks.org/handle/20.500.12854/76855
ER  -