Abstract
Recent advances in metabolic profiling techniques allow global profiling of metabolites in cells, tissues, or organisms, using a wide range of analytical techniques such as nuclear magnetic resonance (NMR) spectroscopy and mass spectrometry (MS). The raw data acquired from these instruments are abundant with technical and structural complexity, which makes it statistically difficult to extract meaningful information. Preprocessing involves various computational procedures where data from the instruments (gas chromatography (GC)/liquid chromatography (LC)-MS, NMR spectra) are converted into a usable form for further analysis and biological interpretation. This chapter covers the common data preprocessing techniques used in metabonomics and is primarily focused on baseline correction, normalization, scaling, peak alignment, detection, and quantification. Recent years have witnessed development of several software tools for data preprocessing, and an overview of the frequently used tools in data preprocessing pipeline is covered.
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Acknowledgments
The author thanks Tone F. Bathen, Guro F. Giskeødegård, and Maria D. Cao for their valuable suggestions and inputs while preparing the manuscript.
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Vettukattil, R. (2015). Preprocessing of Raw Metabonomic Data. In: Bjerrum, J. (eds) Metabonomics. Methods in Molecular Biology, vol 1277. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-2377-9_10
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DOI: https://doi.org/10.1007/978-1-4939-2377-9_10
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