NormalizeMets: assessing, selecting and implementing statistical methods for normalizing metabolomics data

Alysha M De Livera; Gavriel Olshansky; Julie A Simpson; Darren J Creek

doi:10.1007/s11306-018-1347-7

NormalizeMets: assessing, selecting and implementing statistical methods for normalizing metabolomics data

Metabolomics. 2018 Mar 20;14(5):54. doi: 10.1007/s11306-018-1347-7.

Authors

Alysha M De Livera¹, Gavriel Olshansky², Julie A Simpson³, Darren J Creek⁴

Affiliations

¹ Biostatistics Unit, Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, VIC, 3800, Australia. alyshad@unimelb.edu.au.
² Department of Mathematics and Statistics, The University of Melbourne, Melbourne, VIC, 3800, Australia.
³ Biostatistics Unit, Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, VIC, 3800, Australia.
⁴ Drug Delivery, Disposition and Dynamics, Monash Institute of Pharmaceutical Sciences, Monash University, Parkville, VIC, 3052, Australia.

PMID: 30830328
DOI: 10.1007/s11306-018-1347-7

Abstract

Introduction: In metabolomics studies, unwanted variation inevitably arises from various sources. Normalization, that is the removal of unwanted variation, is an essential step in the statistical analysis of metabolomics data. However, metabolomics normalization is often considered an imprecise science due to the diverse sources of variation and the availability of a number of alternative strategies that may be implemented.

Objectives: We highlight the need for comparative evaluation of different normalization methods and present software strategies to help ease this task for both data-oriented and biological researchers.

Methods: We present NormalizeMets-a joint graphical user interface within the familiar Microsoft Excel and freely-available R software for comparative evaluation of different normalization methods. The NormalizeMets R package along with the vignette describing the workflow can be downloaded from https://cran.r-project.org/web/packages/NormalizeMets/ . The Excel Interface and the Excel user guide are available on https://metabolomicstats.github.io/ExNormalizeMets .

Results: NormalizeMets allows for comparative evaluation of normalization methods using criteria that depend on the given dataset and the ultimate research question. Hence it guides researchers to assess, select and implement a suitable normalization method using either the familiar Microsoft Excel and/or freely-available R software. In addition, the package can be used for visualisation of metabolomics data using interactive graphical displays and to obtain end statistical results for clustering, classification, biomarker identification adjusting for confounding variables, and correlation analysis.

Conclusion: NormalizeMets is designed for comparative evaluation of normalization methods, and can also be used to obtain end statistical results. The use of freely-available R software offers an attractive proposition for programming-oriented researchers, and the Excel interface offers a familiar alternative to most biological researchers. The package handles the data locally in the user's own computer allowing for reproducible code to be stored locally.

Keywords: Excel; Normalization; R; Software; Statistical analysis.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Animals
Cluster Analysis
Data Accuracy
Data Interpretation, Statistical
Humans
Mass Spectrometry / methods
Metabolomics / methods*
Metabolomics / standards*
Reference Standards*
Research / standards
Research Design / standards
Software
Surveys and Questionnaires
User-Computer Interface
Workflow