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|Title:||An integrated workflow for robust alignment and simplified quantitative analysis of NMR spectrometry data||Authors:||Vu, Trung N.
Verwaest, Kim A.
|Issue Date:||2011||Publisher:||BIOMED CENTRAL LTD||Source:||BMC BIOINFORMATICS, 12||Abstract:||Background: Nuclear magnetic resonance spectroscopy (NMR) is a powerful technique to reveal and compare quantitative metabolic profiles of biological tissues. However, chemical and physical sample variations make the analysis of the data challenging, and typically require the application of a number of preprocessing steps prior to data interpretation. For example, noise reduction, normalization, baseline correction, peak picking, spectrum alignment and statistical analysis are indispensable components in any NMR analysis pipeline. Results: We introduce a novel suite of informatics tools for the quantitative analysis of NMR metabolomic profile data. The core of the processing cascade is a novel peak alignment algorithm, called hierarchical Cluster-based Peak Alignment (CluPA). The algorithm aligns a target spectrum to the reference spectrum in a top-down fashion by building a hierarchical cluster tree from peak lists of reference and target spectra and then dividing the spectra into smaller segments based on the most distant clusters of the tree. To reduce the computational time to estimate the spectral misalignment, the method makes use of Fast Fourier Transformation (FFT) cross-correlation. Since the method returns a high-quality alignment, we can propose a simple methodology to study the variability of the NMR spectra. For each aligned NMR data point the ratio of the between-group and within-group sum of squares (BW-ratio) is calculated to quantify the difference in variability between and within predefined groups of NMR spectra. This differential analysis is related to the calculation of the F-statistic or a one-way ANOVA, but without distributional assumptions. Statistical inference based on the BW-ratio is achieved by bootstrapping the null distribution from the experimental data. Conclusions: The workflow performance was evaluated using a previously published dataset. Correlation maps, spectral and grey scale plots show clear improvements in comparison to other methods, and the down-to-earth quantitative analysis works well for the CluPA-aligned spectra. The whole workflow is embedded into a modular and statistically sound framework that is implemented as an R package called "speaq" ("spectrum alignment and quantitation"), which is freely available from http://code.google.com/p/speaq/.||Notes:||Vu, TN (reprint author),[Vu, Trung N.; Smets, Koen; Verschoren, Alain; Goethals, Bart; Laukens, Kris] Univ Antwerp, Dept Math & Comp Sci, B-2020 Antwerp, Belgium. [Valkenborg, Dirk] Vlaamse Instelling Technol Onderzoek, B-2400 Mol, Belgium. [Verwaest, Kim A.; Dommisse, Roger; Lemiere, Filip] Univ Antwerp, Dept Chem, B-2020 Antwerp, Belgium. [Vu, Trung N.; Laukens, Kris] Univ Antwerp, Biomed Informat Res Ctr Antwerp Biomina, B-2020 Antwerp, Belgium. [Valkenborg, Dirk] Hasselt Univ, Interuniv Inst Biostat & Stat Bioinformat, Diepenbeek, Belgium. email@example.com||Keywords:||Biochemical Research Methods; Biotechnology & Applied Microbiology; Mathematical & Computational Biology; peak alignment; mass-spectrometry; chromatographic data; cross-correlation; H1-NMR spectra; classification; algorithms; signals||Document URI:||http://hdl.handle.net/1942/12877||ISSN:||1471-2105||e-ISSN:||1471-2105||DOI:||10.1186/1471-2105-12-405||ISI #:||000297044500001||Rights:||© 2011 Vu et al; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.||Category:||A1||Type:||Journal Contribution||Validations:||ecoom 2012|
|Appears in Collections:||Research publications|
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