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Time-resolved biomarker discovery inH-NMR data using generalized fuzzy Hough transform alignment and parallel factor analysis
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.
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2010 (English)In: Analytical and Bioanalytical Chemistry, ISSN 1618-2642, E-ISSN 1618-2650, Vol. 396, no 5, 1681-1689 p.Article in journal (Refereed) Published
Abstract [en]

This work addresses the subject of time-series analysis of comprehensive 1H-NMR data of biological origin. One of the problems with toxicological and efficacy studies is the confounding of correlation between the administered drug, its metabolites and the systemic changes in molecular dynamics, i.e., the flux of drug-related molecules correlates with the molecules of system regulation. This correlation poses a problem for biomarker mining since this confounding must be untangled in order to separate true biomarker molecules from dose-related molecules. One way of achieving this goal is to perform pharmacokinetic analysis. The difference in pharmacokinetic time profiles of different molecules can aid in the elucidation of the origin of the dynamics, this can even be achieved regardless of whether the identity of the molecule is known or not. This mode of analysis is the basis for metabonomic studies of toxicology and efficacy. One major problem concerning the analysis of 1H-NMR data generated from metabonomic studies is that of the peak positional variation and of peak overlap. These phenomena induce variance in the data, obscuring the true information content and are hence unwanted but hard to avoid. Here, we show that by using the generalized fuzzy Hough transform spectral alignment, variable selection, and parallel factor analysis, we can solve both the alignment and the confounding problem stated above. Using the outlined method, several different temporal concentration profiles can be resolved and the majority of the studied molecules and their respective fluxes can be attributed to these resolved kinetic profiles. The resolved time profiles hereby simplifies finding true biomarkers and bio-patterns for early detection of biological conditions as well as providing more detailed information about the studied biological system. The presented method represents a significant step forward in time-series analysis of biological 1H-NMR data as it provides almost full automation of the whole data analysis process and is able to analyze over 800 unique features per sample. The method is demonstrated using a 1H-NMR rat urine dataset from a toxicology study and is compared with a classical approach: COW alignment followed by bucketing.

Place, publisher, year, edition, pages
2010. Vol. 396, no 5, 1681-1689 p.
Keyword [en]
Urine, 1H-NMR, Alignment, Multivariate, Metabolic profiling, PARAFAC, Drug metabolism, Toxicology
National Category
Analytical Chemistry
Research subject
Analytical Chemistry
Identifiers
URN: urn:nbn:se:su:diva-54042DOI: 10.1007/s00216-009-3421-5OAI: oai:DiVA.org:su-54042DiVA: diva2:391837
Available from: 2011-01-25 Created: 2011-01-25 Last updated: 2017-12-11Bibliographically 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
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Available from: 2012-05-03 Created: 2012-03-16 Last updated: 2012-05-02Bibliographically approved

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