Despite the recent advances in the standardization of untargeted metabolomics workflows, there is still a lack of attention to specific data treatment strategies that require deep knowledge of the biological problem and need to be applied after a well-thought out process to understand the effect of the practice. One of those strategies is data normalization. Data-driven assumptions are critical especially addressing unwanted variation present in the biological model as it can be the case in heterogeneous tissues, cells with different sizes or biofluids with different concentrations. Chronic kidney disease (CKD) is a widespread disorder affecting kidney structure and function. Animal models are being developed to be able to get valuable insights into the etiopathogenesis of the condition and effect of the treatments. Moreover, diagnosis and disease staging still require defining appropriate biomarkers. Untargeted metabolomics has the potential to deal with those challenges. Renal fibrosis is one of the consequences of kidney injury which greatly affects the concentration of metabolites in the same quantity of sample. To overcome this challenge, several data normalization strategies have been applied, following a multilevel normalization method with the overall aim of focussing on the relevant biological information and reducing the influence of disturbing factors. A comprehensive evaluation of the performance of the normalization strategies, both on methods assessing the intragroup variation and on the impact on differential analysis, is provided. Finally, we present evidence of the importance of biological-model-driven guided normalization methods and discuss multiple criteria that need to be taken into consideration to obtain robust and reliable data. Special concern is transmitted on the misleading conclusions that might be the consequence of inappropriate data pre-treatment solutions applied for untargeted methods.
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Bicinchoninic acid assay
Capillary electrophoresis–mass spectrometry
Chronic kidney disease
Control genetically modified with the FAO gain-of-function group
Control wild-type group
Fatty acid oxidation
Median fold change
Hierarchical cluster analysis
Jack-knifing uncertainty measures
Obstruction genetically modified with FAO gain-of-function group
Obstruction wild-type group
Orthogonal partial least squares discriminant analysis
Principal component analysis
Partial least squares-discriminant analysis
Probabilistic quotient normalization
Quality control samples and support vector regression correction
Relative log abundance
Relative log expression
Relative standard deviation
Time of flight
Total useful signal
Unilateral ureteral obstruction
Variable importance in projection
Complete data matrix, all samples from experimental groups, QC samples included
All samples from experimental groups, QC samples excluded
Matrix associated only with QC samples
Matrix divided into two groups, (1) control group: CTWT and CTMOD; (2) obstruction group: OBSWT and OBSMOD
Matrix divided into four groups: (1) CTWT; (2) CTMOD; (3) OBSWT; (4) OBSMOD
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This work was supported by Comunidad de Madrid (B-2017/BMD-3751 “NOVELREN-CM”), Ministerio de Ciencia, Innovación y Universidades (RTI 2018-095166-B-100) and Ministerio de Economía y Competitividad (MINECO) SAF2015-66107-R (SL), cofunded by the European Regional Development Fund and Instituto de Salud Carlos III REDinREN RD12/0021/0009 and RD16/0009/0016 (SL).
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Cuevas-Delgado, P., Dudzik, D., Miguel, V. et al. Data-dependent normalization strategies for untargeted metabolomics—a case study. Anal Bioanal Chem 412, 6391–6405 (2020). https://doi.org/10.1007/s00216-020-02594-9
- Unwanted variation
- Data pre-treatment
- Tissue samples
- Capillary electrophoresis mass spectrometry
- Biomarker discovery