Peak alignment using reduced set mapping

Torgrip, Ralf J. O.; Åberg, Magnus; Karlberg, Bo; Jacobsson, Sven P.

doi:10.1002/cem.824

Cited by 90 publications

(78 citation statements)

References 22 publications

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“…Previous studies have shown that the variation of peak location in different data sets is nonlinear [21], [10]. The example in [28] shows that this variation still exists even when we use technical replicates.…”

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confidence: 85%

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Multiple Peak Alignment in Sequential Data Analysis: A Scale-Space-Based Approach

Liu

et al. 2006

IEEE/ACM Trans. Comput. Biol. and Bioinf.

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Abstract-In this paper, we address the multiple peak alignment problem in sequential data analysis with an approach based on the Gaussian scale-space theory. We assume that multiple sets of detected peaks are the observed samples of a set of common peaks. We also assume that the locations of the observed peaks follow unimodal distributions (e.g., normal distribution) with their means equal to the corresponding locations of the common peaks and variances reflecting the extension of their variations. Under these assumptions, we convert the problem of estimating locations of the unknown number of common peaks from multiple sets of detected peaks into a much simpler problem of searching for local maxima in the scale-space representation. The optimization of the scale parameter is achieved using an energy minimization approach. We compare our approach with a hierarchical clustering method using both simulated data and real mass spectrometry data. We also demonstrate the merit of extending the binary peak detection method (i.e., a candidate is considered either as a peak or as a nonpeak) with a quantitative scoring measure-based approach (i.e., we assign to each candidate a possibility of being a peak).

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confidence: 85%

“…Dynamic programming (DP) based approaches [14], [21] have also been proposed. DP has been used in gene expression analysis to warp one gene expression time series to a similar series obtained from a different biological replicate [1], where the correspondence between the two gene expression time series is guaranteed.…”

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confidence: 99%

Multiple Peak Alignment in Sequential Data Analysis: A Scale-Space-Based Approach

Liu

et al. 2006

IEEE/ACM Trans. Comput. Biol. and Bioinf.

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“…Alignment approaches either aim at the detection of corresponding peaks among all spectra (cf. [13]) or modify the exact position of peaks for maximization of similarity among all spectra (cf. [14]).…”

Section: Alignment Of Sroismentioning

confidence: 99%

Automatic Classification of NMR Spectra by Ensembles of Local Experts

Lienemann

Plötz

Fink

2008

Lecture Notes in Computer Science

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Abstract.A new approach for the automatic detection of druginduced organ toxicities based on Nuclear Magnetic Resonance Spectroscopy data from biofluids is presented in this paper. Spectral data from biofluids contain information on the concentration of various substances, but the combination of only a small subset of these cues is putatively useful for classification of new samples. We propose to divide the spectra into several short regions and train classifiers on them, using only a limited amount of information for class discrimination. These local experts are combined in an ensemble classification system and the subset of experts for the final classification is optimized automatically. Thus, only local experts for relevant spectral regions are used for the final ensemble classification. The proposed approach has been evaluated on a real data-set from industrial pharmacology, showing an improvement in classification accuracy and indicating relevant spectral regions for classification.

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“…2. Other methods include genetic algorithm-based methods (Forshed et al 2002;Lee and Woodruff 2004), partial linear fit (Vogels et al 1996), reduced set mapping (Torgrip et al 2003), and principal component analysis-based methods (Stoyanova et al 2004). One common problem with the existing methods is that they often disregard the existence of noise, which is typically observed in NMR spectra.…”

Section: Introductionmentioning

confidence: 99%

A Bayesian approach for the alignment of high-resolution NMR spectra

Kim

Wang

Hiremath³

2008

Ann Oper Res

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Metabolic analysis with high-resolution nuclear magnetic resonance (NMR) enables simultaneous investigation of numerous chemical species in response to biochemical changes in subjects. When the analysis involves comparing two or more NMR spectra, it is essential to properly align them because small variations across different spectra influence the alignment and thus, interfere with direct comparisons between samples. We propose a new alignment method within the Bayesian modeling framework. The proposed method allows us to estimate the amplitude and phase shifts simultaneously and to obtain robust results in the existence of noise. Effectiveness of our proposed method is demonstrated through real NMR spectra in human plasma and a comparison study with dynamic time warping and correlated optimized warping, two widely used alignment methods in spectral data.

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Peak alignment using reduced set mapping

Cited by 90 publications

References 22 publications

Multiple Peak Alignment in Sequential Data Analysis: A Scale-Space-Based Approach

Multiple Peak Alignment in Sequential Data Analysis: A Scale-Space-Based Approach

Automatic Classification of NMR Spectra by Ensembles of Local Experts

A Bayesian approach for the alignment of high-resolution NMR spectra

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