Skip Navigation


Bioinformatics Advance Access originally published online on April 15, 2004
Bioinformatics 2004 20(15):2438-2446; doi:10.1093/bioinformatics/bth268
This Article
Right arrow FREE Full Text (Print PDF) Freely available
Right arrow FREE Full Text (Screen PDF)
Right arrow All Versions of this Article:
20/15/2438    most recent
bth268v1
Right arrow Alert me when this article is cited
Right arrow Alert me if a correction is posted
Services
Right arrow Email this article to a friend
Right arrow Similar articles in this journal
Right arrow Similar articles in ISI Web of Science
Right arrow Similar articles in PubMed
Right arrow Alert me to new issues of the journal
Right arrow Add to My Personal Archive
Right arrow Download to citation manager
Right arrow Search for citing articles in:
ISI Web of Science (19)
Right arrowRequest Permissions
Google Scholar
Right arrow Articles by Jansen, J. J.
Right arrow Articles by Smilde, A. K.
Right arrow Search for Related Content
PubMed
Right arrow PubMed Citation
Right arrow Articles by Jansen, J. J.
Right arrow Articles by Smilde, A. K.
Social Bookmarking
Add to CiteULike   Add to Connotea   Add to Del.icio.us  
What's this?

Bioinformatics 20(15) © Oxford University Press 2004; all rights reserved.

Analysis of longitudinal metabolomics data

Jeroen J. Jansen 1, Huub C. J. Hoefsloot 1, Hans F. M. Boelens 1, Jan van der Greef 2,3 and Age K. Smilde 1,2,*

1 Biosystems Data Analysis, Faculty of Sciences, University of Amsterdam, Nieuwe Achtergracht 166, 1018 WV Amsterdam, The Netherlands, 2 TNO Nutrition and Food Research, PO Box 360, 3700 AJ Zeist, The Netherlands and 3 Beyond Genomics, 40 Bear Hill Road, Waltham, MA 02451, USA

Received on December 1, 2003; revised on March 9, 2004; accepted on April 1, 2004
Advance Access Publication April 15, 2004

Motivation: Metabolomics datasets are generally large and complex. Using principal component analysis (PCA), a simplified view of the variation in the data is obtained. The PCA model can be interpreted and the processes underlying the variation in the data can be analysed. In metabolomics, often a priori information is present about the data. Various forms of this information can be used in an unsupervised data analysis with weighted PCA (WPCA). A WPCA model will give a view on the data that is different from the view obtained using PCA, and it will add to the interpretation of the information in a metabolomics dataset.

Results: A method is presented to translate spectra of repeated measurements into weights describing the experimental error. These weights are used in the data analysis with WPCA. The WPCA model will give a view on the data where the non-uniform experimental error is accounted for. Therefore, the WPCA model will focus more on the natural variation in the data.

Availability: M-files for MATLAB for the algorithm used in this research are available at http://www-its.chem.uva.nl/research/pac/Software/pcaw.zip

Contact: asmilde{at}science.uva.nl

* To whom correspondence should be addressed.


Add to CiteULike CiteULike   Add to Connotea Connotea   Add to Del.icio.us Del.icio.us    What's this?


This article has been cited by other articles:


Home page
 Physiol. GenomicsHome page
Y. Noguchi, N. Shikata, Y. Furuhata, T. Kimura, and M. Takahashi
Characterization of dietary protein-dependent amino acid metabolism by linking free amino acids with transcriptional profiles through analysis of correlation
Physiol Genomics, August 14, 2008; 34(3): 315 - 326.
[Abstract] [Full Text] [PDF]

Home page
 Proc. Natl. Acad. Sci. USAHome page
M. Assfalg, I. Bertini, D. Colangiuli, C. Luchinat, H. Schafer, B. Schutz, and M. Spraul
Evidence of different metabolic phenotypes in humans
PNAS, February 5, 2008; 105(5): 1420 - 1424.
[Abstract] [Full Text] [PDF]

Home page
 Brief BioinformHome page
J. M. Lee, E. P. Gianchandani, and J. A. Papin
Flux balance analysis in the era of metabolomics
Brief Bioinform, June 1, 2006; 7(2): 140 - 150.
[Abstract] [Full Text] [PDF]

Home page
 Toxicol SciHome page
D. G. Robertson
Metabonomics in Toxicology: A Review
Toxicol. Sci., June 1, 2005; 85(2): 809 - 822.
[Abstract] [Full Text] [PDF]


Disclaimer:
Please note that abstracts for content published before 1996 were created through digital scanning and may therefore not exactly replicate the text of the original print issues. All efforts have been made to ensure accuracy, but the Publisher will not be held responsible for any remaining inaccuracies. If you require any further clarification, please contact our Customer Services Department.