Dataset Information

Osmolality-based normalization enhances statistical discrimination of untargeted metabolomic urine analysis: results from a comparative study

ABSTRACT:

INTRODUCTION: Because of its ease of collection, urine is one of the most commonly used matrices for metabolomics studies. However, unlike other biofluids, urine exhibits tremendous variability that can introduce confounding inconsistency during result interpretation. Despite many existing techniques to normalize urine samples, there is still no consensus on either which method is most appropriate or how to evaluate these methods.

OBJECTIVES: To investigate the impact of several methods and combinations of methods conventionally used in urine metabolomics on the statistical discrimination of two groups in a simple metabolomics study.

METHODS: We applied 14 different strategies of normalization to 40 urine samples analysed by liquid chromatography coupled to high-resolution mass spectrometry (LC-HRMS). To evaluate the impact of these different strategies, we relied on the ability of each method to reduce confounding variability while retaining variability of interest, as well as the predictability of statistical models.

RESULTS: Among all tested normalization methods, osmolality-based normalization gave the best results. Moreover, we demonstrated that normalization using a specific dilution prior to the analysis outperformed post-acquisition normalization. We also demonstrated that the combination of various normalization methods does not necessarily improve statistical discrimination.

CONCLUSIONS: This study re-emphasized the importance of normalizing urine samples for metabolomics studies. In addition, it appeared that the choice of method had a significant impact on result quality. Consequently, we suggest osmolality-based normalization as the best method for normalizing urine samples.

INSTRUMENT(S): Liquid Chromatography MS - negative - reverse phase

SUBMITTER: Loic Mervant

PROVIDER: MTBLS1765 | MetaboLights | 2023-09-04

REPOSITORIES: MetaboLights

ACCESS DATA

Publications

Osmolality-based normalization enhances statistical discrimination of untargeted metabolomic urine analysis: results from a comparative study.

Mervant Loïc L Tremblay-Franco Marie M Jamin Emilien L EL Kesse-Guyot Emmanuelle E Galan Pilar P Martin Jean-François JF Guéraud Françoise F Debrauwer Laurent L

Metabolomics : Official journal of the Metabolomic Society 20210102 1

<h4>Introduction</h4>Because of its ease of collection, urine is one of the most commonly used matrices for metabolomics studies. However, unlike other biofluids, urine exhibits tremendous variability that can introduce confounding inconsistency during result interpretation. Despite many existing techniques to normalize urine samples, there is still no consensus on either which method is most appropriate or how to evaluate these methods.<h4>Objectives</h4>To investigate the impact of several met ...[more]

PMID: 33389209

Similar Datasets

Project description:Profiling miRNA levels in cells with miRNA-microarrays is becoming a widely used technique. Although normalization methods for mRNA gene expression arrays are well established, miRNA array normalization has so far not been investigated in detail. In this study we investigate the impact of normalization on data generated with the Agilent miRNA array platform. Here, we developed a method to select non-changing miRNAs (“invariants”) and used them to compute linear regression normalization coefficients or Variance Stabilizing Normalization (VSN) parameters. We compared the invariant normalizations to normalization by, scaling, quantile and VSN with default parameters as well as to no normalization using samples with strong differential expression of miRNAs (heart-brain comparison) and samples where only few miRNAs are affected (p53 overexpression in SCC13 cells versus GFP vector control transfected cells). All normalization methods performed better than no normalization. Normalizations procedures based on the set of invariants and quantile were the most robust over all experimental conditions tested. Our method of invariant selection and normalization is not limited to Agilent miRNA arrays and can be applied to other datasets from one color miRNA microarray platforms, focused gene expression arrays and gene expression analysis using quantitative PCR. Keywords: miRNA profiling To assay technical reproducibility, 3 technical replicates from brain and heart RNA (Stratagene) were hybridized on Agilent human miRNA microarrays. To determine sensitivity and specificity, heart and brain RNA were mixed in the following ratios: 50% heart 50% brain, 25% heart 75% brain and 5% heart 95% brain. Each of the dilutions was hybridized in a technical duplicate on Agilent human miRNA arrays. To assay the effect of p53 expression on miRNA levels in the human SCC13 cell line, RNA from 3 biological replicates of p53-expressing or control cells were hybridized in technical duplicates on the microarrays, resulting in a total of 12 hybridizations.

Dataset Information

Osmolality-based normalization enhances statistical discrimination of untargeted metabolomic urine analysis: results from a comparative study

Publications

Osmolality-based normalization enhances statistical discrimination of untargeted metabolomic urine analysis: results from a comparative study.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure