Normalization techniques for PARAFAC modeling of urine metabolomic data

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989592%3A15310%2F16%3A33159173" target="_blank" >RIV/61989592:15310/16:33159173 - isvavai.cz</a>

Alternative codes found

RIV/61989592:15110/16:33159173

Result on the web

<a href="http://link.springer.com/article/10.1007/s11306-016-1059-9" target="_blank" >http://link.springer.com/article/10.1007/s11306-016-1059-9</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/s11306-016-1059-9" target="_blank" >10.1007/s11306-016-1059-9</a>

Result language

angličtina

Original language name

Normalization techniques for PARAFAC modeling of urine metabolomic data

Original language description

The concentrations of metabolites in urine (one of the body fluids used in metabolomics studies) are affected by hydration status of an individual, resulting in dilution differences. This requires therefore normalization of the data to correct for such differences. Two normalization techniques are commonly applied to urine samples prior to their further statistical analysis. First, AUC normalization aims to normalize a group of signals with peaks by standardizing the area under the curve (AUC) within a sample to the median, mean or any other proper representation of the amount of dilution. The second approach uses specific end-product metabolites such as creatinine and all intensities within a sample are expressed relative to the creatinine intensity. Another way of looking at urine metabolomics data is by realizing that the ratios between peak intensities are the information-carrying features. This opens up possibilities to use another class of data analysis techniques designed to deal with such ratios: compositional data analysis. The aim of this paper is to develop PARAFAC modeling of three-way urine metabolomics data in the context of compositional data analysis and compare this with standard normalization techniques.In the compositional data analysis approach, special coordinate systems are defined to deal with the ratio problem. In essence, it comes down to using other distance measures than the Euclidian Distance that is used in the conventional analysis of metabolomic data. We illustrate using this type of approach in combination with three-way methods (i.e. PARAFAC) of a longitudinal urine metabolomics study and two simulations. In both cases, the advantage of the compositional approach is established in terms of improved interpretability of the scores and loadings of the PARAFAC model. For urine metabolomics studies, we advocate the use of compositional data analysis approaches. They are easy to use, well established and proof to give reliable results.

Czech name

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Czech description

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Type

J_x - Unclassified - Peer-reviewed scientific article (Jimp, Jsc and Jost)

CEP classification

BB - Applied statistics, operational research

OECD FORD branch

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Project

Result was created during the realization of more than one project. More information in the Projects tab.

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year

2016

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

Metabolomics

ISSN

1573-3882

e-ISSN

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Volume of the periodical

12

Issue of the periodical within the volume

7

Country of publishing house

US - UNITED STATES

Number of pages

13

Pages from-to

"nestránkováno"

UT code for WoS article

000379508500009

EID of the result in the Scopus database

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