- A preview of this full-text is provided by Springer Nature.
- Learn more

Preview content only

Content available from Metabolomics

This content is subject to copyright. Terms and conditions apply.

ORIGINAL ARTICLE

Normalization techniques for PARAFAC modeling of urine

metabolomic data

Alz

ˇbe

ˇta Gardlo

1,2

•Age K. Smilde

3

•Karel Hron

1

•Marcela Hrda

´

2

•

Radana Karlı

´kova

´

2

•David Friedecky

´

2,4

•Toma

´s

ˇAdam

2,4

Received: 5 January 2016 / Accepted: 14 June 2016 / Published online: 23 June 2016

ÓSpringer Science+Business Media New York 2016

Abstract

Introduction One of the body ﬂuids often used in meta-

bolomics studies is urine. The concentrations of metabo-

lites in urine 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.

Objectives 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 pos-

sibilities 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.

Methods 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.

Results We illustrate using this type of approach in com-

bination with three-way methods (i.e. PARAFAC) of a

longitudinal urine metabolomics study and two simula-

tions. In both cases, the advantage of the compositional

approach is established in terms of improved inter-

pretability of the scores and loadings of the PARAFAC

model.

Conclusion 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.

Keywords Parallel factor analysis (PARAFAC)

Compositional data Metabolomics Creatinine Area

under the curve

1 Introduction

Metabolomics as a young subﬁeld of the omics sciences

concerns the comprehensive characterization of metabo-

lites in biological systems. It is applied to plants, bacteria,

animals and humans; in humans all biological materials

from bioﬂuids (blood, urine) till tissues can be analyzed.

Metabolomics is increasingly being used in almost all

ﬁelds of health science including pharmacology, pre-clin-

ical drug trials, toxicology, newborn screening and many

&Alz

ˇbe

ˇta Gardlo

alzbeta.gardlo@gmail.com

1

Department of Mathematical Analysis and Applications of

Mathematics, Faculty of Science, Palacky

´University,

Olomouc, Czech Republic

2

Laboratory of Metabolomics, Institute of Molecular and

Translational Medicine, University Hospital Olomouc,

Palacky

´University Olomouc, Olomouc, Czech Republic

3

Biosystems Data Analysis, Swammerdam Institute for Life

Sciences, University of Amsterdam, Amsterdam, The

Netherlands

4

Department of Clinical Biochemistry, University Hospital

Olomouc, Olomouc, Czech Republic

123

Metabolomics (2016) 12:117

DOI 10.1007/s11306-016-1059-9

Content courtesy of Springer Nature, terms of use apply. Rights reserved.