Normalization techniques for PARAFAC modeling of urine metabolomic data

Abstract

Introduction One of the body fluids often used in metabolomics studies is urine. The concentrations of metabolites 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 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. 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 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. 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.

SDA: a semi-parametric differential abundance analysis method for metabolomics and proteomics data

“…In fact, data normalization is a critical step in MS data processing to adjust size effect, due to the difference in the sample amount or dilution across samples, as well as other technical variations. Various data normalization methods, such as housekeeping normalization [18, 25, 26], centred logratio transformation [25], probabilistic quotient normalization [25, 27], total sum normalization [25], and variance stabilization normalization [27, 28], have been proposed. The choice of an appropriate normalization method depends on the type of biological samples, the study design, and the investigator’s experience.…”

“…The choice of an appropriate normalization method depends on the type of biological samples, the study design, and the investigator’s experience. It has been shown that data normalization can substantially affect downstream analysis [25, 28, 29]. Therefore, we highly suggest users to carefully perform data normalization prior to differential abundance analysis.…”

Ziziphus jujuba Mill. var. spinosa (Bunge) Hu ex H. F. Chou Seed Ameliorates Insomnia in Rats by Regulating Metabolomics and Intestinal Flora Composition

“…The parameters of width at 5% height and peak-to-peak baseline noise were automatically calculated. The total peak area was normalized according the reference ( Gardlo et al, 2016 ), that is, the ion intensities for each detected peak were normalized against the sum of the peak intensities within the sample. The data lists of RT, m/z and normalized peak area of each peak in positive and negative ion mode were generated, respectively.…”

Chemometric Strategies for Peak Detection and Profiling from Multidimensional Chromatography

“…Different approaches aim to correct this undesired size effect using either chemical or mathematical normalizations . As methods used for this normalization in multidimensional separations are analogous to those used in monodimensional separations, interested readers are referred to recent detailed reviews …”