Files and links (1)
Published (Version of Record)CC BY V4.0, Open Access
Journal article
Statistical HOmogeneous Cluster SpectroscopY (SHOCSY): An Optimized Statistical Approach for Clustering of H-1 NMR Spectral Data to Reduce Interference and Enhance Robust Biomarkers Selection
Analytical chemistry (Washington), Vol.86(11), pp.5308-5315
2014
PMID: 24773160
Abstract
We propose a novel statistical approach to improve the reliability of H-1 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 H-1 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 H-1 NMR data set to emulate renal tubule toxicity and further exemplified this method with a H-1 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.
Details
- Title
- Statistical HOmogeneous Cluster SpectroscopY (SHOCSY): An Optimized Statistical Approach for Clustering of H-1 NMR Spectral Data to Reduce Interference and Enhance Robust Biomarkers Selection
- Authors/Creators
- Xin Zou - Medway School of PharmacyElaine Holmes - Imperial College LondonJeremy K. Nicholson - Imperial College LondonRuey Leng Loo - Medway School of Pharmacy
- Publication Details
- Analytical chemistry (Washington), Vol.86(11), pp.5308-5315
- Publisher
- Amer Chemical Soc
- Number of pages
- 8
- Grant note
- G1002151 / Medical Research Council; UK Research & Innovation (UKRI); Medical Research Council UK (MRC); European Commission G1002151 / MRC; UK Research & Innovation (UKRI); Medical Research Council UK (MRC)
- Identifiers
- 991005598619207891
- Copyright
- © 2014 American Chemical Society
- Murdoch Affiliation
- Centre for Computational and Systems Medicine
- Language
- English
- Resource Type
- Journal article
UN Sustainable Development Goals (SDGs)
This output has contributed to the advancement of the following goals:
Source: InCites
Metrics
45 Record Views
InCites Highlights
These are selected metrics from InCites Benchmarking & Analytics tool, related to this output
- Collaboration types
- Domestic collaboration
- Citation topics
- 2 Chemistry
- 2.211 Mass Spectrometry
- 2.211.990 Metabolomics
- Web Of Science research areas
- Chemistry, Analytical
- ESI research areas
- Chemistry