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Statistical HOmogeneous Cluster SpectroscopY (SHOCSY): an optimized statistical approach for clustering of ¹H NMR spectral data to reduce interference and enhance robust biomarkers selection.

Zou, Xin, Holmes, Elaine, Nicholson, Jeremy K., Loo, Ruey Leng (2014) Statistical HOmogeneous Cluster SpectroscopY (SHOCSY): an optimized statistical approach for clustering of ¹H NMR spectral data to reduce interference and enhance robust biomarkers selection. Analytical Chemistry, 86 (11). pp. 5308-5315. ISSN 0003-2700. (doi:10.1021/ac500161k) (KAR id:56006)

Abstract

We propose a novel statistical approach to improve the reliability of (1)H NMR spectral analysis in complex metabolic studies. The Statistical HOmogeneous Cluster SpectroscopY (SHOCSY) algorithm aims to reduce the variation within biological classes by selecting subsets of homogeneous (1)H NMR spectra that contain specific spectroscopic metabolic signatures related to each biological class in a study. In SHOCSY, we used a clustering method to categorize the whole data set into a number of clusters of samples with each cluster showing a similar spectral feature and hence biochemical composition, and we then used an enrichment test to identify the associations between the clusters and the biological classes in the data set. We evaluated the performance of the SHOCSY algorithm using a simulated (1)H NMR data set to emulate renal tubule toxicity and further exemplified this method with a (1)H NMR spectroscopic study of hydrazine-induced liver toxicity study in rats. The SHOCSY algorithm improved the predictive ability of the orthogonal partial least-squares discriminatory analysis (OPLS-DA) model through the use of "truly" representative samples in each biological class (i.e., homogeneous subsets). This method ensures that the analyses are no longer confounded by idiosyncratic responders and thus improves the reliability of biomarker extraction. SHOCSY is a useful tool for removing irrelevant variation that interfere with the interpretation and predictive ability of models and has widespread applicability to other spectroscopic data, as well as other "omics" type of data.

Item Type: Article
DOI/Identification number: 10.1021/ac500161k
Subjects: Q Science > QA Mathematics (inc Computing science) > QA 76 Software, computer programming,
Divisions: Divisions > Division of Natural Sciences > Medway School of Pharmacy
Funders: [UNSPECIFIED] MRC
Depositing User: Rueyleng Loo
Date Deposited: 22 Jun 2016 07:54 UTC
Last Modified: 16 Feb 2021 13:35 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/56006 (The current URI for this page, for reference purposes)

University of Kent Author Information

Loo, Ruey Leng.

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