Data-dependent normalization strategies for untargeted metabolomics—a case study

Abstract

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.

Toxicometabolomics: Small Molecules to Answer Big Toxicological Questions

“…Subsequently, in order to reduce the unwanted systematic bias, so that only biologically relevant variations are present in the data and that all samples become comparable in terms of absolute intensities, a normalization step is usually performed [ 103 ]. The selection of an appropriate normalization method depends on the type of sample to be analyzed and, over time, several methods have been reported; namely, the use of one or multiple internal standards, total metabolite signal, total protein content, DNA concentration, osmolality, urine creatinine, and probabilistic quotient normalization (PQN), among other algorithms [ 103 , 104 , 105 , 106 ]. The main strengths and limitations of each normalization method have been described elsewhere [ 103 , 107 ].…”

Parallel Metabolomic Profiling of Cerebrospinal Fluid, Plasma, and Spinal Cord to Identify Biomarkers for Spinal Cord Injury

“…The obtained data matrix was then manually inspected in Microsoft Excel (Microsoft Office 2010). Statistical analysis was conducted by retaining only such LC-MS features, as shared by more than 50% of samples (Cuevas-Delgado et al 2020). The peak intensity of individual LC-MS feature in each sample was normalized to peak intensity of total useful signal (Cuevas-Delgado et al 2020).…”

“…Statistical analysis was conducted by retaining only such LC-MS features, as shared by more than 50% of samples (Cuevas-Delgado et al 2020). The peak intensity of individual LC-MS feature in each sample was normalized to peak intensity of total useful signal (Cuevas-Delgado et al 2020). Multivariate (PCA and PLS-DA) and univariate (ANOVA) statistical analyses were carried out by the metaboanalyst (www.…”

Food Phenotyping: Recording and Processing of Non-Targeted Liquid Chromatography Mass Spectrometry Data for Verifying Food Authenticity

“…This constant is the median of all detected signal intensities of a sample. Compared to the mean value, the median has the advantage that it is more robust against individual outliers [ 103 , 104 , 105 ].…”