Metabolomics Data Processing and Data Analysis—Current Best Practices

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 Met...

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Detalhes bibliográficos
Corporate Authors: Hanhineva, Kati, Van der Hooft, Justin
Formato: Livro
Idioma:Inglês
Publicado em: MDPI - Multidisciplinary Digital Publishing Institute 2021
Assuntos:
thema EDItEUR::G Reference, Information and Interdisciplinary subjects::GP Research and information: general
metabolic networks
mass spectral libraries
metabolite annotation
metabolomics data mapping
nontarget analysis
liquid chromatography mass spectrometry
compound identification
tandem mass spectral library
forensics
wastewater
gut microbiome
meta-omics
metagenomics
metabolomics
metabolic reconstructions
genome-scale metabolic modeling
constraint-based modeling
flux balance
host–microbiome
metabolism
global metabolomics
LC-MS
spectra processing
pathway analysis
enrichment analysis
mass spectrometry
liquid chromatography
MS spectral prediction
metabolite identification
structure-based chemical classification
rule-based fragmentation
combinatorial fragmentation
time series
PLS
NPLS
variable selection
bootstrapped-VIP
data repository
computational metabolomics
reanalysis
lipidomics
data processing
triplot
multivariate risk modeling
environmental factors
disease risk
chemical classification
in silico workflows
metabolome mining
molecular families
networking
substructures
mass spectrometry imaging
metabolomics imaging
biostatistics
ion selection algorithms
liquid chromatography high-resolution mass spectrometry
data-independent acquisition
all ion fragmentation
targeted analysis
untargeted analysis
R programming
full-scan MS/MS processing
R-MetaboList 2
liquid chromatography–mass spectrometry (LC/MS)
fragmentation (MS/MS)
data-dependent acquisition (DDA)
simulator
in silico
untargeted metabolomics
liquid chromatography–mass spectrometry (LC-MS)
experimental design
sample preparation
univariate and multivariate statistics
metabolic pathway and network analysis
LC–MS
metabolic profiling
computational statistical
unsupervised learning
supervised learning
Acesso em linha:https://directory.doabooks.org/handle/20.500.12854/76855
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