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A Conversation on Data Mining Strategies in LC-MS Untargeted Metabolomics: Pre-Processing and Pre-Treatment Steps

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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

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Here is the evidence, now what is the hypothesis? The complementary roles of inductive and hypothesis‐driven science in the post‐genomic era

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CV‐ANOVA for significance testing of PLS and OPLS® models

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Harnessing the complexity of metabolomic data with chemometrics

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A chemometrics toolbox based on projections and latent variables

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Chemometrics for the analysis of chromatographic data in metabolomics investigations

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Centering and scaling in component analysis

inferredcites →https://doi.org/10.1002/cem.941openalex-snapshot

Symbiosis of chemometrics and metabolomics: past, present, and future

inferredcites →https://doi.org/10.1002/elps.201300053openalex-snapshot

From numbers to a biological sense: H ow the strategy chosen for metabolomics data treatment may affect final results. A practical example based on urine fingerprints obtained by LC ‐ MS

inferredcites →https://doi.org/10.1002/elps.201500352openalex-snapshot

Missing value imputation strategies for metabolomics data

inferredcites →https://doi.org/10.1002/elps.201500417openalex-snapshot

Updates in metabolomics tools and resources: 2014–2015

inferredcites →https://doi.org/10.1002/jssc.200900609openalex-snapshot

Knowledge discovery in metabolomics: An overview of MS data handling

inferredcites →https://doi.org/10.1002/pca.2513openalex-snapshot

Metabolomics: What You See is What You Extract

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Investigation of analytical variation in metabonomic analysis using liquid chromatography/mass spectrometry

inferredcites →https://doi.org/10.1002/rcm.7094openalex-snapshot

Comparison of peak‐picking workflows for untargeted liquid chromatography/high‐resolution mass spectrometry metabolomics data analysis

inferredcites →https://doi.org/10.1002/rcm.7526openalex-snapshot

Preferential alkali metal adduct formation by cis geometrical isomers of dicaffeoylquinic acids allows for efficient discrimination from their trans isomers during ultra‐high‐performance liquid chromatography/quadrupole time‐of‐flight mass spectrometry

inferredcites →https://doi.org/10.1007/s00216-009-2662-7openalex-snapshot

Parameter selection for peak alignment in chromatographic sample profiling: objective quality indicators and use of control samples

inferredcites →https://doi.org/10.1007/s00299-013-1461-yopenalex-snapshot

The phytohormone crosstalk paradigm takes center stage in understanding how plants respond to abiotic stresses

inferredcites →https://doi.org/10.1007/s11306-005-1106-4openalex-snapshot

A metabolome pipeline: from concept to data to knowledge

inferredcites →https://doi.org/10.1007/s11306-006-0037-zopenalex-snapshot

Statistical strategies for avoiding false discoveries in metabolomics and related experiments

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Minimum reporting standards for plant biology context information in metabolomic studies

inferredcites →https://doi.org/10.1007/s11306-007-0081-3openalex-snapshot

Proposed minimum reporting standards for data analysis in metabolomics

inferredcites →https://doi.org/10.1007/s11306-007-0082-2openalex-snapshot

Proposed minimum reporting standards for chemical analysis

inferredcites →https://doi.org/10.1007/s11306-007-0099-6openalex-snapshot

Assessment of PLSDA cross validation

inferredcites →https://doi.org/10.1007/s11306-009-0191-1openalex-snapshot

Dynamic metabolomic data analysis: a tutorial review

inferredcites →https://doi.org/10.1007/s11306-011-0324-1openalex-snapshot

The biological interpretation of metabolomic data can be misled by the extraction method used

inferredcites →https://doi.org/10.1007/s11306-013-0598-6openalex-snapshot

Reflections on univariate and multivariate analysis of metabolomics data

inferredcites →https://doi.org/10.1007/s11306-014-0712-4openalex-snapshot

Controlling the quality of metabolomics data: new strategies to get the best out of the QC sample

inferredcites →https://doi.org/10.1007/s11306-014-0738-7openalex-snapshot

The influence of scaling metabolomics data on model classification accuracy

inferredcites →https://doi.org/10.1007/s11306-014-0742-yopenalex-snapshot

Experiment design beyond gut feeling: statistical tests and power to detect differential metabolites in mass spectrometry data

inferredcites →https://doi.org/10.1007/s11306-015-0879-3openalex-snapshot

Data standards can boost metabolomics research, and if there is a will, there is a way

inferredcites →https://doi.org/10.1007/s11306-016-1030-9openalex-snapshot

Non-targeted UHPLC-MS metabolomic data processing methods: a comparative investigation of normalisation, missing value imputation, transformation and scaling

inferredcites →https://doi.org/10.1007/s11816-011-0191-2openalex-snapshot

Recent advances of metabolomics in plant biotechnology

inferredcites →https://doi.org/10.1016/b978-0-12-394446-7.00027-3openalex-snapshot

Mass Spectrometry Metabolomic Data Handling for Biomarker Discovery

inferredcites →https://doi.org/10.1016/b978-0-12-397922-3.00011-3openalex-snapshot

Strategies for Data Handling and Statistical Analysis in Metabolomics Studies

inferredcites →https://doi.org/10.1016/j.chemolab.2004.12.011openalex-snapshot

Performance of some variable selection methods when multicollinearity is present

inferredcites →https://doi.org/10.1016/j.chemolab.2012.07.010openalex-snapshot

A review of variable selection methods in Partial Least Squares Regression

inferredcites →https://doi.org/10.1016/j.chroma.2014.06.100openalex-snapshot

Comparative evaluation of preprocessing freeware on chromatography/mass spectrometry data for signature discovery

inferredcites →https://doi.org/10.1016/j.copbio.2014.10.001openalex-snapshot

Biological insights through nontargeted metabolomics

inferredcites →https://doi.org/10.1016/j.febslet.2009.11.024openalex-snapshot

A unifying view of 21st century systems biology

inferredcites →https://doi.org/10.1016/j.jchromb.2005.07.049openalex-snapshot

Metabolic fingerprinting of rat urine by LC/MSPart 2. Data pretreatment methods for handling of complex data

inferredcites →https://doi.org/10.1016/j.jchromb.2009.08.041openalex-snapshot

Joint GC–MS and LC–MS platforms for comprehensive plant metabolomics: Repeatability and sample pre-treatment

inferredcites →https://doi.org/10.1016/j.tibtech.2004.03.007openalex-snapshot

Metabolomics by numbers: acquiring and understanding global metabolite data

inferredcites →https://doi.org/10.1016/j.tibtech.2012.10.011openalex-snapshot

Network analysis: tackling complex data to study plant metabolism

inferredcites →https://doi.org/10.1016/j.tplants.2014.05.005openalex-snapshot

Plant single-cell and single-cell-type metabolomics

inferredcites →https://doi.org/10.1016/j.trac.2007.08.003openalex-snapshot

Metabolomics technologies and metabolite identification

inferredcites →https://doi.org/10.1016/s0031-9422(02)00708-2openalex-snapshot

Plant metabolomics: large-scale phytochemistry in the functional genomics era

inferredcites →https://doi.org/10.1016/s0169-7439(96)00007-xopenalex-snapshot

Missing data methods in PCA and PLS: Score calculations with incomplete observations

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Multivariate process and quality monitoring applied to an electrolysis process

inferredcites →https://doi.org/10.1021/ac0713510openalex-snapshot

Visualization of GC/TOF-MS-Based Metabolomics Data for Identification of Biochemically Interesting Compounds Using OPLS Class Models

inferredcites →https://doi.org/10.1021/ac102981kopenalex-snapshot

Expanding Coverage of the Metabolome for Global Metabolite Profiling

inferredcites →https://doi.org/10.1021/ac103011bopenalex-snapshot

Technical and Biological Variation in UPLC−MS-Based Untargeted Metabolic Profiling of Liver Extracts: Application in an Experimental Toxicity Study on Galactosamine

inferredcites →https://doi.org/10.1021/ac201065jopenalex-snapshot

Optimized Preprocessing of Ultra-Performance Liquid Chromatography/Mass Spectrometry Urinary Metabolic Profiles for Improved Information Recovery

inferredcites →https://doi.org/10.1021/ac502439yopenalex-snapshot

Statistical Methods for Handling Unwanted Variation in Metabolomics Data

inferredcites →https://doi.org/10.1021/ci025626iopenalex-snapshot

Assessing Model Fit by Cross-Validation

inferredcites →https://doi.org/10.1021/ci0342472openalex-snapshot

The Problem of Overfitting

inferredcites →https://doi.org/10.1021/pr060594qopenalex-snapshot

Chemometrics in Metabonomics

inferredcites →https://doi.org/10.1038/nbt1041openalex-snapshot

A proposed framework for the description of plant metabolomics experiments and their results

inferredcites →https://doi.org/10.1038/nprot.2011.335openalex-snapshot

Procedures for large-scale metabolic profiling of serum and plasma using gas chromatography and liquid chromatography coupled to mass spectrometry

inferredcites →https://doi.org/10.1039/b604498kopenalex-snapshot

A pragmatic and readily implemented quality control strategy for HPLC-MS and GC-MS-based metabonomic analysis

inferredcites →https://doi.org/10.1039/c4mb00414kopenalex-snapshot

PLS/OPLS models in metabolomics: the impact of permutation of dataset rows on the K-fold cross-validation quality parameters

inferredcites →https://doi.org/10.1073/pnas.102102699openalex-snapshot

Selection bias in gene extraction on the basis of microarray gene-expression data

inferredcites →https://doi.org/10.1080/00031305.1999.10474474openalex-snapshot

Dot Plots

inferredcites →https://doi.org/10.1093/bioinformatics/btu290openalex-snapshot

MIRA: mutual information-based reporter algorithm for metabolic networks

inferredcites →https://doi.org/10.1093/database/bat029openalex-snapshot

The MetaboLights repository: curation challenges in metabolomics

inferredcites →https://doi.org/10.1093/nar/gks1004openalex-snapshot

MetaboLights—an open-access general-purpose repository for metabolomics studies and associated meta-data

inferredcites →https://doi.org/10.1111/j.1399-3054.2007.01001.xopenalex-snapshot

Metabolomic technologies and their application to the study of plants and plant–host interactions

inferredcites →https://doi.org/10.1186/1471-2164-7-142openalex-snapshot

Centering, scaling, and transformations: improving the biological information content of metabolomics data

inferredcites →https://doi.org/10.1214/ss/1009213726openalex-snapshot

Statistical Modeling: The Two Cultures (with comments and a rejoinder by the author)

inferredcites →https://doi.org/10.1371/journal.pone.0087846openalex-snapshot

Multi-Platform Metabolomic Analyses of Ergosterol-Induced Dynamic Changes in Nicotiana tabacum Cells

inferredcites →https://doi.org/10.1586/14737159.2015.974562openalex-snapshot

Getting the right answers: understanding metabolomics challenges

inferredcites →https://doi.org/10.1590/sajs.2013/20120005openalex-snapshot

Plant metabolomics: A new frontier in phytochemical analysis

inferredcites →https://doi.org/10.2174/138920209789177638openalex-snapshot

Review of Peak Detection Algorithms in Liquid-Chromatography-Mass Spectrometry

inferredcites →https://doi.org/10.2174/2213235x11301010092openalex-snapshot

Multivariate Analysis in Metabolomics

inferredcites →https://doi.org/10.3389/fmolb.2015.00004openalex-snapshot

A data preprocessing strategy for metabolomics to reduce the mask effect in data analysis

inferredcites →https://doi.org/10.4155/bio.12.210openalex-snapshot

Maximizing The Value of Metabolomic Data

inferredcites →https://doi.org/10.5936/csbj.201301006openalex-snapshot

ERROR ANALYSIS AND PROPAGATION IN METABOLOMICS DATA ANALYSIS

inferredcites →https://doi.org/10.5936/csbj.201301009openalex-snapshot

STATISTICAL METHODS FOR THE ANALYSIS OF HIGH-THROUGHPUT METABOLOMICS DATA

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

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Habituated Moringa oleifera callus retains metabolic responsiveness to external plant growth regulators

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Simulated aging processes induced significant changes in redox properties of biochar for chromate reduction

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Prediction of fatty acid composition in camellia oil by 1H NMR combined with PLS regression

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Critical assessment of chemometric models employed for varietal authentication of wine based on UHPLC-HRMS data

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Allelopathic metabolite accumulation and microbial dysbiosis underlying continuous cropping disorder in the Panax ginseng rhizosphere

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Ultrasonic effects on the headspace volatilome and protein isolate microstructure of duck liver, as well as their potential correlation mechanism

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Five Easy Metrics of Data Quality for LC–MS-Based Global Metabolomics

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How Low Can You Go? Selecting Intensity Thresholds for Untargeted Metabolomics Data Preprocessing

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Toward Automated Preprocessing of Untargeted LC-MS-Based Metabolomics Feature Lists from Human Biofluids

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Multilaboratory Untargeted Mass Spectrometry Metabolomics Collaboration to Identify Bottlenecks and Comprehensively Annotate A Single Dataset

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LCMS-Net: Deep Learning for Raw High Resolution Mass Spectrometry Data Applied to Forensic Cause-of-Death Screening

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Multiomic Signatures of Traffic-Related Air Pollution in London Reveal Potential Short-Term Perturbations in Gut Microbiome-Related Pathways

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Clinically distinct metabotypes of pediatric MASLD identified through unsupervised clustering of NASH CRN data

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Pharmacometabonomics: data processing and statistical analysis

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The plant-beneficial fungus Trichoderma harzianum T22 modulates plant metabolism and negatively affects Nezara viridula

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Data-Driven UPLC-Orbitrap MS Analysis in Astrochemistry

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Metabolomic Characterisation of Discriminatory Metabolites Involved in Halo Blight Disease in Oat Cultivars Caused by Pseudomonas syringae pv. coronafaciens

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Profiling of Volatile Organic Compounds from Four Plant Growth-Promoting Rhizobacteria by SPME–GC–MS: A Metabolomics Study

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Seed Metabolomic Landscape Reflecting Key Differential Metabolic Profiles Among Different Wheat Cultivars

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A Tool to Encourage Minimum Reporting Guideline Uptake for Data Analysis in Metabolomics

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From Samples to Insights into Metabolism: Uncovering Biologically Relevant Information in LC-HRMS Metabolomics Data

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Metabolomics Combined with Multivariate Statistical Analysis for Screening of Chemical Markers between Gentiana scabra and Gentiana rigescens

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inferred← citeshttps://doi.org/10.3390/ph16111568openalex-snapshot

Pharmacokinetics in Pharmacometabolomics: Towards Personalized Medication

inferred← citeshttps://doi.org/10.3390/plants10030437openalex-snapshot

Application of Plant Growth Regulators Modulates the Profile of Chlorogenic Acids in Cultured Bidens pilosa Cells

inferred← citeshttps://doi.org/10.3390/plants14223532openalex-snapshot

Biochemical Responses of Atacama and Blesbok Sweet Potato (Ipomoea batatas L.) Cultivars to Early Drought Stress

inferred← citeshttps://doi.org/10.3390/plants9101275openalex-snapshot

Profiling of Altered Metabolomic States in Bidens pilosa Leaves in Response to Treatment by Methyl Jasmonate and Methyl Salicylate

inferred← citeshttps://doi.org/10.3390/toxins11120723openalex-snapshot

Metabolome Variation between Strains of Microcystis aeruginosa by Untargeted Mass Spectrometry

inferred← citeshttps://doi.org/10.5772/intechopen.69171openalex-snapshot

Food Metabolomics: A New Frontier in Food Analysis and its Application to Understanding Fermented Foods

inferred← citeshttps://doi.org/10.6084/m9.figshare.16655841openalex-snapshot

Additional file 1 of Altered metabolomic states elicited by Flg22 and FlgII-28 in Solanum lycopersicum: intracellular perturbations and metabolite defenses

inferred← citeshttps://doi.org/10.6084/m9.figshare.26589825openalex-snapshot

Additional file 1 of Disruption of fish gut microbiota composition and holobiont’s metabolome during a simulated Microcystis aeruginosa (Cyanobacteria) bloom

inferred← citeshttps://doi.org/10.6084/m9.figshare.26589828openalex-snapshot

Additional file 2 of Disruption of fish gut microbiota composition and holobiont’s metabolome during a simulated Microcystis aeruginosa (Cyanobacteria) bloom

inferred← citeshttps://doi.org/10.6084/m9.figshare.26589831openalex-snapshot

Additional file 3 of Disruption of fish gut microbiota composition and holobiont’s metabolome during a simulated Microcystis aeruginosa (Cyanobacteria) bloom

inferred← citeshttps://doi.org/10.6084/m9.figshare.26589834openalex-snapshot

Additional file 4 of Disruption of fish gut microbiota composition and holobiont’s metabolome during a simulated Microcystis aeruginosa (Cyanobacteria) bloom

inferred← citeshttps://doi.org/10.6084/m9.figshare.26589837openalex-snapshot

Additional file 5 of Disruption of fish gut microbiota composition and holobiont’s metabolome during a simulated Microcystis aeruginosa (Cyanobacteria) bloom

inferred← citeshttps://doi.org/10.6084/m9.figshare.26589840openalex-snapshot

Additional file 6 of Disruption of fish gut microbiota composition and holobiont’s metabolome during a simulated Microcystis aeruginosa (Cyanobacteria) bloom

inferred← citeshttps://doi.org/10.70675/03773980zafafz4515za869z1eff7ac07d72openalex-snapshot

Development of non-targeted approaches to evidence emerging chemical hazard : Identification of new biomarkers of internal human exposure, in order to support human biomonitoring and the study of the link between chemical exposure and human health

inferred← citeshttps://doi.org/10.70675/83217131z5737z4581za8dezf43d07c96729openalex-snapshot

Utilisation d'une métabolomique non ciblée dans l'urine des humains pour identifier les biomarqueurs de l'ingestion de Genipap (Genipa americana L.)

inferred← citeshttps://doi.org/10.70675/bf09e78azcb98z43d0z9b93z4e5bd3bd61d8openalex-snapshot

Methodological developments for the non-targeted characterization of the human internal chemical exposome in epidemiological studies : optimizing and implementing a high-throughput workflow for the identification of new biomarkers of exposure in blood plasma and serum samples

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