Localized Deconvolution: Characterizing NMR-based Metabolomics Spectroscopic Data using Localized High-throughput Deconvolution

TitleLocalized Deconvolution: Characterizing NMR-based Metabolomics Spectroscopic Data using Localized High-throughput Deconvolution
Publication TypeConference Paper
Year of Publication2012
AuthorsPaul Anderson, Ajith Ranabahu, Deirdre Mahle, Nicholas Reo, Michael Raymer, Amit Sheth, Nicholas J. DelRaso
Conference NameInternational Conference on Bioinformatics & Computational Biology
Conference LocationLas Vegas, Nevada, USA
Keywordsdeconvolution, Hadoop, localized deconvolution, NMR
Abstract

The interpretation of nuclear magnetic resonance (NMR) experimental results for metabolomics studies requires intensive signal processing and multivariate data analysis techniques. Standard quantification techniques attempt to minimize effects from variations in peak positions caused by sample pH, ionic strength, and composition. These techniques fail to account for adjacent signals which can lead to drastic quantification errors. Attempts at full spectrum deconvolution have been limited in adoption and development due to the computational resources required. Herein, we develop a novel localized deconvolution algorithm for general purpose quantification of NMR-based metabolomics studies. Localized deconvolution decreases average absolute quantification error by 97% and average relative quantification error by 88%. When applied to a 1H metabolomics study, the cross-validation metric, Q2, improved 16% by reducing within group variability. This increase in accuracy leads to additional computing costs that are overcome by translating the algorithm to the mapreduce design paradigm.

Full Text

Paul E. Anderson, Ajith H. Ranabahu, Deirdre A. Mahle, Nicholas V. Reo, Michael L. Raymer, Amit P. Sheth, and Nicholas J. DelRaso. 'Localized Deconvolution: Characterizing NMR-based Metabolomics Spectroscopic Data using Localized High-throughput Deconvolution' at International Conference on Bioinformatics & Computational Biology (BioComp), 2012. to appear.
project: SCALE

venue name: International Conference on Bioinformatics & Computational Biology

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