Literature DB >> 20339444

Dynamic metabolomic data analysis: a tutorial review.

A K Smilde, J A Westerhuis, H C J Hoefsloot, S Bijlsma, C M Rubingh, D J Vis, R H Jellema, H Pijl, F Roelfsema, J van der Greef.   

Abstract

In metabolomics, time-resolved, dynamic or temporal data is more and more collected. The number of methods to analyze such data, however, is very limited and in most cases the dynamic nature of the data is not even taken into account. This paper reviews current methods in use for analyzing dynamic metabolomic data. Moreover, some methods from other fields of science that may be of use to analyze such dynamic metabolomics data are described in some detail. The methods are put in a general framework after providing a formal definition on what constitutes a 'dynamic' method. Some of the methods are illustrated with real-life metabolomics examples.

Entities:  

Year:  2009        PMID: 20339444      PMCID: PMC2834778          DOI: 10.1007/s11306-009-0191-1

Source DB:  PubMed          Journal:  Metabolomics        ISSN: 1573-3882            Impact factor:   4.290


  30 in total

1.  Analysis of longitudinal metabolomics data.

Authors:  Jeroen J Jansen; Huub C J Hoefsloot; Hans F M Boelens; Jan van der Greef; Age K Smilde
Journal:  Bioinformatics       Date:  2004-04-15       Impact factor: 6.937

2.  ANOVA-simultaneous component analysis (ASCA): a new tool for analyzing designed metabolomics data.

Authors:  Age K Smilde; Jeroen J Jansen; Huub C J Hoefsloot; Robert-Jan A N Lamers; Jan van der Greef; Marieke E Timmerman
Journal:  Bioinformatics       Date:  2005-05-12       Impact factor: 6.937

Review 3.  Metabolomics-based systems biology and personalized medicine: moving towards n = 1 clinical trials?

Authors:  Jan van der Greef; Thomas Hankemeier; Robert N McBurney
Journal:  Pharmacogenomics       Date:  2006-10       Impact factor: 2.533

4.  Expression and role of the corticotropin-releasing hormone/urocortin-receptor-binding protein system in the primate corpus luteum during the menstrual cycle.

Authors:  Jing Xu; Fuhua Xu; Jon D Hennebold; Theodore A Molskness; Richard L Stouffer
Journal:  Endocrinology       Date:  2007-08-09       Impact factor: 4.736

5.  Analyzing longitudinal microbial metabolomics data.

Authors:  Carina M Rubingh; Sabina Bijlsma; Renger H Jellema; Karin M Overkamp; Mariët J van der Werf; Age K Smilde
Journal:  J Proteome Res       Date:  2009-09       Impact factor: 4.466

6.  Estimating dynamic models for gene regulation networks.

Authors:  Jiguo Cao; Hongyu Zhao
Journal:  Bioinformatics       Date:  2008-05-27       Impact factor: 6.937

7.  Endocrine pulse identification using penalized methods and a minimum set of assumptions.

Authors:  Daniel J Vis; Johan A Westerhuis; Huub C J Hoefsloot; Hanno Pijl; Ferdinand Roelfsema; Jan van der Greef; Age K Smilde
Journal:  Am J Physiol Endocrinol Metab       Date:  2009-10-27       Impact factor: 4.310

8.  Pulsatile LH release is diminished, whereas FSH secretion is normal, in hypocretin-deficient narcoleptic men.

Authors:  S W Kok; F Roelfsema; S Overeem; G J Lammers; M Frölich; A E Meinders; H Pijl
Journal:  Am J Physiol Endocrinol Metab       Date:  2004-06-01       Impact factor: 4.310

9.  Statistical validation of megavariate effects in ASCA.

Authors:  Daniel J Vis; Johan A Westerhuis; Age K Smilde; Jan van der Greef
Journal:  BMC Bioinformatics       Date:  2007-08-30       Impact factor: 3.169

10.  Piecewise multivariate modelling of sequential metabolic profiling data.

Authors:  Mattias Rantalainen; Olivier Cloarec; Timothy M D Ebbels; Torbjörn Lundstedt; Jeremy K Nicholson; Elaine Holmes; Johan Trygg
Journal:  BMC Bioinformatics       Date:  2008-02-19       Impact factor: 3.169

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  33 in total

1.  Between Metabolite Relationships: an essential aspect of metabolic change.

Authors:  Jeroen J Jansen; Ewa Szymańska; Huub C J Hoefsloot; Doris M Jacobs; Katrin Strassburg; Age K Smilde
Journal:  Metabolomics       Date:  2011-05-24       Impact factor: 4.290

2.  A statistical framework for biomarker discovery in metabolomic time course data.

Authors:  Maurice Berk; Timothy Ebbels; Giovanni Montana
Journal:  Bioinformatics       Date:  2011-07-15       Impact factor: 6.937

3.  Inferring causal relationships between phenotypes using summary statistics from genome-wide association studies.

Authors:  Xiang-He Meng; Hui Shen; Xiang-Ding Chen; Hong-Mei Xiao; Hong-Wen Deng
Journal:  Hum Genet       Date:  2018-02-19       Impact factor: 4.132

4.  Statistical analysis in metabolic phenotyping.

Authors:  Benjamin J Blaise; Gonçalo D S Correia; Gordon A Haggart; Izabella Surowiec; Caroline Sands; Matthew R Lewis; Jake T M Pearce; Johan Trygg; Jeremy K Nicholson; Elaine Holmes; Timothy M D Ebbels
Journal:  Nat Protoc       Date:  2021-07-28       Impact factor: 13.491

5.  Feedback-based, system-level properties of vertebrate-microbial interactions.

Authors:  Ariel L Rivas; Mark D Jankowski; Renata Piccinini; Gabriel Leitner; Daniel Schwarz; Kevin L Anderson; Jeanne M Fair; Almira L Hoogesteijn; Wilfried Wolter; Marcelo Chaffer; Shlomo Blum; Tom Were; Stephen N Konah; Prakash Kempaiah; John M Ong'echa; Ulrike S Diesterbeck; Rachel Pilla; Claus-Peter Czerny; James B Hittner; James M Hyman; Douglas J Perkins
Journal:  PLoS One       Date:  2013-02-20       Impact factor: 3.240

6.  Modeling and Classification of Kinetic Patterns of Dynamic Metabolic Biomarkers in Physical Activity.

Authors:  Marc Breit; Michael Netzer; Klaus M Weinberger; Christian Baumgartner
Journal:  PLoS Comput Biol       Date:  2015-08-28       Impact factor: 4.475

7.  Metabolite analysis distinguishes between mice with epidermolysis bullosa acquisita and healthy mice.

Authors:  Sarah Schönig; Andreas Recke; Misa Hirose; Ralf J Ludwig; Karsten Seeger
Journal:  Orphanet J Rare Dis       Date:  2013-06-26       Impact factor: 4.123

8.  Towards polypharmacokinetics: pharmacokinetics of multicomponent drugs and herbal medicines using a metabolomics approach.

Authors:  Ke Lan; Guoxiang Xie; Wei Jia
Journal:  Evid Based Complement Alternat Med       Date:  2013-03-14       Impact factor: 2.629

Review 9.  Metabolomics and systems pharmacology: why and how to model the human metabolic network for drug discovery.

Authors:  Douglas B Kell; Royston Goodacre
Journal:  Drug Discov Today       Date:  2013-07-26       Impact factor: 7.851

10.  Multivariate statistical models of metabolomic data reveals different metabolite distribution patterns in isonitrosoacetophenone-elicited Nicotiana tabacum and Sorghum bicolor cells.

Authors:  Ntakadzeni E Madala; Lizelle A Piater; Paul A Steenkamp; Ian A Dubery
Journal:  Springerplus       Date:  2014-05-20
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