Unsupervised data analysis methods used in qualitative and quantitative metabolomics and metabonomics

From National Research Council Canada

DOIResolve DOI: http://doi.org/10.4018/978-1-61350-435-2.ch001
AuthorSearch for:
TypeBook Chapter
Book titleSystemic Approaches in Bioinformatics and Computational Systems Biology
Series titleAdvances in Bioinformatics and Biomedical Engineering
ISSN2327-7033
2327-7041
ISBN9781613504352
9781613504369
Pages1–28
Abstract
Metabolomics or metababonomics is one of the major high throughput analysis methods that endeavors holistic measurement of metabolic profiles of biological systems. Data analysis approaches in metabolomics can broadly be divided into qualitative – analysis of spectral data and quantitative – analysis of individual metabolite concentrations. In this work, the author will demonstrate the benefits and limitations of different unsupervised analysis tools currently utilized in qualitative and quantitative metabolomics data analysis. Following a detailed literature review outlining different applications of unsupervised methods in metabolomics, the author shows examples of an application of the major previously utilized unsupervised analysis methods. The testing of these methods was performed using qualitative as well as corresponding quantitative metabolite data derived to represent a large set of 2,000 objects. Spectra of mixtures were obtained from different combinations of experimental NMR measurements of 13 prevalent metabolites at five different groups of concentrations representing different phenotypes. The analysis shows advantages and disadvantages of standard tools when applied specifically to metabolomics.
Publication date
PublisherMedical Information Science Reference
LanguageEnglish
AffiliationInformation and Communication Technologies; National Research Council Canada
Peer reviewedYes
NPARC number23001800
Export citationExport as RIS
Report a correctionReport a correction
Record identifierc5de7866-858f-4239-bcfe-85d27de1f2a7
Record created2017-04-07
Record modified2017-04-07
Date modified:
Report a problem on this page