Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Automated annotation and quantification of metabolites in (1)H NMR data of biological origin
Stockholm University, Faculty of Science, Department of Analytical Chemistry.
Stockholm University, Faculty of Science, Department of Analytical Chemistry.
Stockholm University, Faculty of Science, Department of Analytical Chemistry.
AstraZeneca R&D Sodertalje, Safety Assessment, Mol Toxicol, S-15185 Sodertalje, Sweden .
Show others and affiliations
2012 (English)In: Analytical and Bioanalytical Chemistry, ISSN 1618-2642, E-ISSN 1618-2650, Vol. 403, no 2, 443-455 p.Article in journal (Refereed) Published
Abstract [en]

In 1H NMR metabolomic datasets, there are often over a thousand peaks per spectrum, many of which change position drastically between samples. Automatic alignment, annotation, and quantification of all the metabolites of interest in such datasets have not been feasible. In this work we propose a fully automated annotation and quantification procedure which requires annotation of metabolites only in a single spectrum. The reference database built from that single spectrum can be used for any number of 1H NMR datasets with a similar matrix. The procedure is based on the generalized fuzzy Hough transform (GFHT) for alignment and on Principal-components analysis (PCA) for peak selection and quantification. We show that we can establish quantities of 21 metabolites in several 1H NMR datasets and that the procedure is extendable to include any number of metabolites that can be identified in a single spectrum. The procedure speeds up the quantification of previously known metabolites and also returns a table containing the intensities and locations of all the peaks that were found and aligned but not assigned to a known metabolite. This enables both biopattern analysis of known metabolites and data mining for new potential biomarkers among the unknowns.

Place, publisher, year, edition, pages
2012. Vol. 403, no 2, 443-455 p.
Keyword [en]
1H NMR, Alignment, Multivariate, Metabolomics, Hough transform, Urine, Quantification, Spectral profiling
National Category
Analytical Chemistry
Research subject
Analytical Chemistry
Identifiers
URN: urn:nbn:se:su:diva-74546DOI: 10.1007/s00216-012-5789-xISI: 000302256800012OAI: oai:DiVA.org:su-74546DiVA: diva2:510534
Available from: 2012-03-16 Created: 2012-03-16 Last updated: 2017-12-07Bibliographically approved
In thesis
1. Solving the correspondence problem in analytical chemistry: Automated methods for alignment and quantification of multiple signals
Open this publication in new window or tab >>Solving the correspondence problem in analytical chemistry: Automated methods for alignment and quantification of multiple signals
2012 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

When applying statistical data analysis techniques to analytical chemical data, all variables must have correspondence over the samples dimension in order for the analysis to generate meaningful results. Peak shifts in NMR and chromatography destroys that correspondence and creates data matrices that have to be aligned before analysis. In this thesis, new methods are introduced that allow for automated transformation from unaligned raw data to aligned data matrices where each column corresponds to a unique signal. These methods are based around linear multivariate models for the peak shifts and Hough transform for establishing the parameters of these linear models. Methods for quantification under difficult conditions, such as crowded spectral regions, noisy data and unknown peak identities are also introduced. These methods include automated peak selection and a robust method for background subtraction. This thesis focuses on the processing of the data; the experimental work is secondary and is not discussed in great detail.

All the developed methods are put together in a full procedure that takes us from raw data to a table of concentrations in a matter of minutes.

The procedure is applied to 1H-NMR data from biological samples, which is one of the toughest alignment tasks available in the field of analytical chemistry. It is shown that the procedure performs consistently on the same level as much more labor intensive manual techniques such as Chenomx NMRSuite spectral profiling.

Several kinds of datasets are evaluated using the procedure. Most of the data is from the field of Metabolomics, where the goal is to establish concentrations of as many small molecules as possible in biological samples.

Place, publisher, year, edition, pages
Stockholm: Department of Analytical Chemistry, Stockholm University, 2012. 74 p.
National Category
Chemical Sciences
Research subject
Analytical Chemistry
Identifiers
urn:nbn:se:su:diva-74556 (URN)978-91-7447-485-5 (ISBN)
Public defence
2012-05-25, Magnélisalen, Kemiska övningslaboratoriet, Svante Arrhenius väg 16 B, Stockholm, 13:00 (English)
Opponent
Supervisors
Available from: 2012-05-03 Created: 2012-03-16 Last updated: 2012-05-02Bibliographically approved

Open Access in DiVA

No full text

Other links

Publisher's full text

Search in DiVA

By author/editor
Alm, ErikÅberg, K. Magnus
By organisation
Department of Analytical Chemistry
In the same journal
Analytical and Bioanalytical Chemistry
Analytical Chemistry

Search outside of DiVA

GoogleGoogle Scholar

doi
urn-nbn

Altmetric score

doi
urn-nbn
Total: 79 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf