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

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

Bibliographic Details
Main Authors:	Di Guida, Riccardo, Engel, Jasper, Allwood, J. William, Weber, Ralf J. M., Jones, Martin R., Sommer, Ulf, Viant, Mark R., Dunn, Warwick B.
Format:	Online
Language:	English
Published:	Springer US 2016
Online Access:	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4831991/

Similar Items

Training needs in metabolomics
by: Weber, Ralf J. M., et al.
Published: (2015)

Metabolomic Profiling of Plasma from Melioidosis Patients Using UHPLC-QTOF MS Reveals Novel Biomarkers for Diagnosis
by: Lau, Susanna K. P., et al.
Published: (2016)

Normalization and missing value imputation for label-free LC-MS analysis
by: Karpievitch, Yuliya V, et al.
Published: (2012)

Quantification of Sterol and Triterpenol Biomarkers in Sediments of the Cananéia-Iguape Estuarine-Lagoonal System (Brazil) by UHPLC-MS/MS
by: Bataglion, Giovana Anceski, et al.
Published: (2016)

MUSCLE: automated multi-objective evolutionary optimization of targeted LC-MS/MS analysis
by: Bradbury, James, et al.
Published: (2015)

Quantitative profiling of sphingolipids in wild Cordyceps and its mycelia by using UHPLC-MS
by: Mi, Jia-Ning, et al.
Published: (2016)

Missing Data: data replacement and imputation
by: Graeme, Hutcheson
Published: (2012)

The role of reporting standards for metabolite annotation and identification in metabolomic studies
by: Salek, Reza M, et al.
Published: (2013)

Analyzing data with missing continuous covariates by multiple imputation using proper imputation
by: Husna Zayadi
Published: (2011)

Missing value imputation in high-dimensional phenomic data: imputable or not, and how?
by: Liao, Serena G, et al.
Published: (2014)

CASMI—The Small Molecule Identification Process from a Birmingham Perspective
by: Allwood, J. William, et al.
Published: (2013)

Comparison of chemical profiles between the root and aerial parts from three Bupleurum species based on a UHPLC-QTOF-MS metabolomics approach
by: Lin Zhu, et al.
Published: (2017-06-01)

Stable isotope dilution assay for the accurate determination of mycotoxins in maize by UHPLC-MS/MS
by: Varga, Elisabeth, et al.
Published: (2012)

Dried blood spot UHPLC-MS/MS analysis of oseltamivir and oseltamivircarboxylate—a validated assay for the clinic
by: Hooff, Gero P., et al.
Published: (2011)

New method of analysis of nitrofurans and nitrofuran metabolites in different biological matrices using UHPLC-MS/MS
by: Śniegocki Tomasz, et al.
Published: (2018-06-01)

Pharmacokinetic comparison between quercetin and quercetin 3-O-β-glucuronide in rats by UHPLC-MS/MS
by: Yang, Le-Le, et al.
Published: (2016)

Secondary Metabolites in Ramalina terebrata Detected by UHPLC/ESI/MS/MS and Identification of Parietin as Tau Protein Inhibitor
by: Cornejo, Alberto, et al.
Published: (2016)

UHPLC/MS-MS Analysis of Six Neonicotinoids in Honey by Modified QuEChERS: Method Development, Validation, and Uncertainty Measurement
by: Proietto Galeano, Michele, et al.
Published: (2013)

Imputing missing genotypes: effects of methods and patterns of missing data
by: Ogut, Funda, et al.
Published: (2011)

Imputing missing values in modelling the PM10 concentrations
by: Nuradhiathy Abd Razak,, et al.
Published: (2014)

Missing variability in meta-analysis : is imputing always good?
by: Nik Idris, Nik Ruzni, et al.
Published: (2006)

Robust imputation method for missing values in microarray data
by: Yoon, Dankyu, et al.
Published: (2007)

Imputation methods for missing data for polygenic models
by: Fridley, Brooke, et al.
Published: (2003)

Comparison of imputation methods for missing laboratory data in medicine
by: Waljee, Akbar K, et al.
Published: (2013)

Fast accurate missing SNP genotype local imputation
by: Wang, Yining, et al.
Published: (2012)

Missing values in data analysis: Ignore or impute?
by: Husna Zayadi
Published: (2011)

Imputing missing values in modelling the PM10 concentrations
by: Lydia Iryani Binti Mohamed Khairul Banks
Published: (2014)

MISSING VALUE IMPUTATION AND NORMALIZATION TECHNIQUES IN MYOCARDIAL INFARCTION
by: K Manimekalai, et al.
Published: (2018-04-01)

Multiple Imputation of Missing Composite Outcomes in Longitudinal Data
by: O’Keeffe, Aidan G., et al.
Published: (2016)

Does the Missing Data Imputation Method Affect the Composition and Performance of Prognostic Models?
by: Baneshi, M R, et al.
Published: (2012)

UHPLC: The Greening Face of Liquid Chromatography
by: Cielecka-Piontek, Judyta, et al.
Published: (2013)

Multiple imputation for IPD meta‐analysis: allowing for heterogeneity and studies with missing covariates
by: Quartagno, M., et al.
Published: (2015)

Integrated LC-MS/MS system for plant metabolomics
by: Sawada, Yuji, et al.
Published: (2013)

Discriminating from species of Curcumae Radix (Yujin) by a UHPLC/Q-TOFMS-based metabolomics approach
by: Liu, Fang, et al.
Published: (2016)

Multiple imputation for handling missing outcome data when estimating the relative risk
by: Thomas R. Sullivan, et al.
Published: (2017-09-01)

Normalisation Of The International Normalised Ratio Prior To Interventional Procedure: Is It Necessary?
by: Mohamad Mahyedin, Mohd Tarmizi
Published: (2017)

Development and validation of a (semi-)quantitative UHPLC-MS/MS method for the determination of 191 mycotoxins and other fungal metabolites in almonds, hazelnuts, peanuts and pistachios
by: Varga, Elisabeth, et al.
Published: (2013)

Simple, sensitive and rapid determination of linifanib (ABT-869), a novel tyrosine kinase inhibitor in rat plasma by UHPLC-MS/MS
by: Iqbal, Muzaffar, et al.
Published: (2014)

Simultaneous Analysis of 20 Mycotoxins in Grapes and Wines from Hexi Corridor Region (China): Based on a QuEChERS–UHPLC–MS/MS Method
by: Bo Zhang, et al.
Published: (2018-08-01)

Site-specific quantification of lysine acetylation in the N-terminal tail of histone H4 using a double-labelling, targeted UHPLC MS/MS approach
by: D’Urzo, Annalisa, et al.
Published: (2016)