| 1. |
- Alm, Erik, 1980-, et al.
(author)
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A solution to the 1D NMR alignment problem using an extended generalized fuzzy Hough transform and mode support
- 2009
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In: Analytical and Bioanalytical Chemistry. - : Springer Science and Business Media LLC. - 1618-2642 .- 1618-2650. ; 395:1, s. 213-223
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Journal article (peer-reviewed)abstract
- This paper approaches the problem of intersample peak correspondence in the context of later applying statistical data analysis techniques to 1D 1H-nuclear magnetic resonance (NMR) data. Any data analysis methodology will fail to produce meaningful results if the analyzed data table is not synchronized, i.e., each analyzed variable frequency (Hz) does not originate from the same chemical source throughout the entire dataset. This is typically the case when dealing with NMR data from biological samples. In this paper, we present a new state of the art for solving this problem using the generalized fuzzy Hough transform (GFHT). This paper describes significant improvements since the method was introduced for NMR datasets of plasma in Csenki et al. (Anal Bioanal Chem 389:875-885, 15) and is now capable of synchronizing peaks from more complex datasets such as urine as well as plasma data. We present a novel way of globally modeling peak shifts using principal component analysis, a new algorithm for calculating the transform and an effective peak detection algorithm. The algorithm is applied to two real metabonomic 1H-NMR datasets and the properties of the method are compared to bucketing. We implicitly prove that GFHT establishes the objectively true correspondence. Desirable features of the GFHT are: (1) intersample peak correspondence even if peaks change order on the frequency axis and (2) the method is symmetric with respect to the samples.
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| 2. |
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| 3. |
- Alm, Erik, 1980-, et al.
(author)
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Time-resolved biomarker discovery inH-NMR data using generalized fuzzy Hough transform alignment and parallel factor analysis
- 2010
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In: Analytical and Bioanalytical Chemistry. - : Springer Science and Business Media LLC. - 1618-2642 .- 1618-2650. ; 396:5, s. 1681-1689
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Journal article (peer-reviewed)abstract
- 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.
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| 4. |
- Alm, Erik, 1980-, et al.
(author)
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Vibrational overtone combination spectroscopy (VOCSY)—a new way of using IR and NIR data
- 2007
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In: Analytical and Bioanalytical Chemistry. - : Springer Science and Business Media LLC. - 1618-2642 .- 1618-2650. ; 388:1, s. 179-188
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Journal article (peer-reviewed)abstract
- This work explores a novel method for rearranging 1st order (one-way) infra-red (IR) and/or near infra-red (NIR) ordinary spectra into a representation suitable for multi-way modelling and analysis. The method is based on the fact that the fundamental IR absorption and the first, second, and consecutive overtones of NIR absorptions represent identical chemical information. It is therefore possible to rearrange these overtone regions of the vectors comprising an IR and NIR spectrum into a matrix where the fundamental, 1st, 2nd, and consecutive overtones of the spectrum are arranged as either rows or columns in a matrix, resulting in a true three-way tensor of data for several samples. This tensorization facilitates explorative analysis and modelling with multi-way methods, for example parallel factor analysis (PARAFAC), N-way partial least squares (N-PLS), and Tucker models. The vibrational overtone combination spectroscopy (VOCSY) arrangement is shown to benefit from the “order advantage”, producing more robust, stable, and interpretable models than, for example, the traditional PLS modelling method. The proposed method also opens the field of NIR for true peak decomposition—a feature unique to the method because the latent factors acquired using PARAFAC can represent pure spectral components whereas latent factors in principal component analysis (PCA) and PLS usually do not.
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| 5. |
- Csenki, Leonard, et al.
(author)
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Proof of principle of a generalized fuzzy Hough transform approach to peak alignment of one-dimensional 1H NMR data
- 2007
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In: Analytical and Bioanalytical Chemistry. - : Springer Science and Business Media LLC. - 1618-2642 .- 1618-2650. ; 389:3, s. 875-885
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Journal article (peer-reviewed)abstract
- In metabolic profiling, multivariate data analysis techniques are used to interpret one-dimensional (1D) 1H NMR data. Multivariate data analysis techniques require that peaks are characterised by the same variables in every spectrum. This location constraint is essential for correct comparison of the intensities of several NMR spectra. However, variations in physicochemical factors can cause the locations of the peaks to shift. The location prerequisite may thus not be met, and so, to solve this problem, alignment methods have been developed. However, current state-of-the-art algorithms for data alignment cannot resolve the inherent problems encountered when analysing NMR data of biological origin, because they are unable to align peaks when the spatial order of the peaks changes—a commonly occurring phenomenon. In this paper a new algorithm is proposed, based on the Hough transform operating on an image representation of the NMR dataset that is capable of correctly aligning peaks when existing methods fail. The proposed algorithm was compared with current state-of-the-art algorithms operating on a selected plasma dataset to demonstrate its potential. A urine dataset was also processed using the algorithm as a further demonstration. The method is capable of successfully aligning the plasma data but further development is needed to address more challenging applications, for example urine data.
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| 7. |
- Stolt, Ragnar, et al.
(author)
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Second-Order Peak Detection for Multicomponent High-Resolution LC/MS Data
- 2006
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In: Analytical Chemistry. - : American Chemical Society (ACS). - 0003-2700 .- 1520-6882. ; 78:4, s. 975-83
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Journal article (peer-reviewed)abstract
- The first step when analyzing multicomponent LC/MS data from complex samples such as biofluid metabolic profiles is to separate the data into information and noise via, for example, peak detection. Due to the complex nature of this type of data, with problems such as alternating backgrounds and differing peak shapes, this can be a very complex task. This paper presents and evaluates a two-dimensional peak detection algorithm based on raw vector-represented LC/MS data. The algorithm exploits the fact that in high-resolution centroid data chromatographic peaks emerge flanked with data voids in the corresponding mass axis. According to the proposed method, only 4‰ of the total amount of data from a urine sample is defined as chromatographic peaks; however, 94% of the raw data variance is captured within these peaks. Compared to bucketed data, results show that essentially the same features that an experienced analyst would define as peaks can automatically be extracted with a minimum of noise and background. The method is simple and requires a priori knowledge of only the minimum chromatographic peak widtha system-dependent parameter that is easily assessed. Additional meta parameters are estimated from the data themselves. The result is well-defined chromatographic peaks that are consistently arranged in a matrix at their corresponding m/z values. In the context of automated analysis, the method thus provides an alternative to the traditional approach of bucketing the data followed by denoising and/or one-dimensional peak detection. The software implementation of the proposed algorithm is available at http://www.anchem.su.se/peakd as compiled code for Matlab.
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| 10. |
- Åberg, K. Magnus, et al.
(author)
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Extensions to peak alignment using reduced set mapping and classification of LC-UV data from peptide mapping
- 2005
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In: Journal of Chemometrics. - : Wiley InterScience. - 0886-9383 .- 1099-128X. ; 18:10, s. 465-473
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Journal article (peer-reviewed)abstract
- Peak alignment using reduced set mapping (PARS) is extended with a new baseline approximation and a new dendrogram alignment scheme, which is designed to avoid the issue of selecting a target chromatogram for the alignment. Two data sets with LC/UV data are studied and it is shown that peak alignment with PARS increases the class separation substantially in the principal component score space. The results indicate that it is possible to use PARS for calibration transfer of multivariate models of chromatographic data.
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