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Non-negative principal component analysis for NMR-based metabolomic data analysis

Proton nuclear magnetic resonance ( 1H-NMR) spectroscopy is one of the major analytical platforms used in metabolomics. The data acquired from NMR experiments are frequently processed using multivariate statistical methods such as principal component analysis (PCA) and partial least squares (PLS) to...

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Main Authors:	Lingli, Deng, Cheng, Kian Kai, Jiyang, Dong, Julian, L. Griffin, Zhong, Chen
Format:	Article
Published:	Elsevier 2012
Subjects:	TP Chemical technology
Online Access:	http://eprints.utm.my/id/eprint/32964/ http://dx.doi.org/10.1016/j.chemolab.2012.07.011
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spelling	my.utm.329642018-11-30T06:33:40Z http://eprints.utm.my/id/eprint/32964/ Non-negative principal component analysis for NMR-based metabolomic data analysis Lingli, Deng Cheng, Kian Kai Jiyang, Dong Julian, L. Griffin Zhong, Chen TP Chemical technology Proton nuclear magnetic resonance ( 1H-NMR) spectroscopy is one of the major analytical platforms used in metabolomics. The data acquired from NMR experiments are frequently processed using multivariate statistical methods such as principal component analysis (PCA) and partial least squares (PLS) to extract biologically meaningful information from complex spectra. Conventionally, these methods produce components with both positive and negative loadings, which contradict with the non-negativity of Fourier-transformed NMR spectra. In recent years, there is an increasing interest in incorporating non-negative constraints into multivariate methods. In the current study, a non-negative principal component analysis (NPCA) algorithm was introduced for the analysis of NMR-based metabolomic data. Using a simulated dataset, we showed that NPCA could reveal interesting local features in multivariate dataset, which are hidden in conventional PCA model. Notably, simulated peaks arising from a single compound were extracted by a same component in NPCA model. The current results also highlighted NPCA to be less susceptible to noise as compared to PCA. Furthermore, a supervised version of NPCA (sNPCA) was developed for class discrimination analysis, and it was used to identify urinary metabolites that distinguished hyperthyroid patients from healthy volunteers. Our results demonstrated that both NPCA and sNPCA could produce easily interpretable results and provide additional information to that of conventional projection methods. Elsevier 2012-08 Article PeerReviewed Lingli, Deng and Cheng, Kian Kai and Jiyang, Dong and Julian, L. Griffin and Zhong, Chen (2012) Non-negative principal component analysis for NMR-based metabolomic data analysis. Chemometrics and Intelligent Laboratory Systems, 118 . pp. 51-61. ISSN 0169-7439 http://dx.doi.org/10.1016/j.chemolab.2012.07.011 DOI:10.1016/j.chemolab.2012.07.011
institution	Universiti Teknologi Malaysia
building	UTM Library
collection	Institutional Repository
continent	Asia
country	Malaysia
content_provider	Universiti Teknologi Malaysia
content_source	UTM Institutional Repository
url_provider	http://eprints.utm.my/
topic	TP Chemical technology
spellingShingle	TP Chemical technology Lingli, Deng Cheng, Kian Kai Jiyang, Dong Julian, L. Griffin Zhong, Chen Non-negative principal component analysis for NMR-based metabolomic data analysis
description	Proton nuclear magnetic resonance ( 1H-NMR) spectroscopy is one of the major analytical platforms used in metabolomics. The data acquired from NMR experiments are frequently processed using multivariate statistical methods such as principal component analysis (PCA) and partial least squares (PLS) to extract biologically meaningful information from complex spectra. Conventionally, these methods produce components with both positive and negative loadings, which contradict with the non-negativity of Fourier-transformed NMR spectra. In recent years, there is an increasing interest in incorporating non-negative constraints into multivariate methods. In the current study, a non-negative principal component analysis (NPCA) algorithm was introduced for the analysis of NMR-based metabolomic data. Using a simulated dataset, we showed that NPCA could reveal interesting local features in multivariate dataset, which are hidden in conventional PCA model. Notably, simulated peaks arising from a single compound were extracted by a same component in NPCA model. The current results also highlighted NPCA to be less susceptible to noise as compared to PCA. Furthermore, a supervised version of NPCA (sNPCA) was developed for class discrimination analysis, and it was used to identify urinary metabolites that distinguished hyperthyroid patients from healthy volunteers. Our results demonstrated that both NPCA and sNPCA could produce easily interpretable results and provide additional information to that of conventional projection methods.
format	Article
author	Lingli, Deng Cheng, Kian Kai Jiyang, Dong Julian, L. Griffin Zhong, Chen
author_facet	Lingli, Deng Cheng, Kian Kai Jiyang, Dong Julian, L. Griffin Zhong, Chen
author_sort	Lingli, Deng
title	Non-negative principal component analysis for NMR-based metabolomic data analysis
title_short	Non-negative principal component analysis for NMR-based metabolomic data analysis
title_full	Non-negative principal component analysis for NMR-based metabolomic data analysis
title_fullStr	Non-negative principal component analysis for NMR-based metabolomic data analysis
title_full_unstemmed	Non-negative principal component analysis for NMR-based metabolomic data analysis
title_sort	non-negative principal component analysis for nmr-based metabolomic data analysis
publisher	Elsevier
publishDate	2012
url	http://eprints.utm.my/id/eprint/32964/ http://dx.doi.org/10.1016/j.chemolab.2012.07.011
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score	13.211869

Non-negative principal component analysis for NMR-based metabolomic data analysis

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