ArticleJournal of pharmaceutical and biomedical analysis2020
Evaluation of statistical techniques to normalize mass spectrometry-based urinary metabolomics data.
Article in Journal of pharmaceutical and biomedical analysis, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
19 citing papers in PubMed.
- Urinary creatinine in very low gestational age neonates: importance for interpretation of creatinine-normalized urinary amino acids.Jornal de pediatria · 2026Article
- Protocol for high-throughput, automated, solid-phase extraction of sphingolipids from human urine.STAR protocols · 2026Article
- Optimized feature selection and zero-parameter channel attention BiLSTM for RPL-attack classification in IoT networks.PloS one · 2026Article
- A Comparative Metabolomics Study of Multiple Urological Diseases by Highly Sensitive Dansylation Isotope Labeling LC-MS.International journal of molecular sciences · 2025Article
- Untargeted metabolomic profiling of serum and urine in kidney cancer: a non-invasive approach for biomarker discovery.Metabolomics : Official journal of the Metabolomic Society · 2025Article
- Epidemiologic study of waterborne and foodborne diseases in Brazil: mapping trends and challenges through a 22-year (2000-2021) historical series.Journal of water and health · 2025Article
- MetaboDirect: an analytical pipeline for the processing of FT-ICR MS-based metabolomic data.Microbiome · 2023Article
- Health associations of liver enzymes and inflammatory scores with urinary citrus flavonoid metabolites.Food & function · 2023Article
- Characteristics of Normalization Methods in Quantitative Urinary Metabolomics-Implications for Epidemiological Applications and Interpretations.Biomolecules · 2022Article
- A Comprehensive Evaluation of Metabolomics Data Preprocessing Methods for Deep Learning.Metabolites · 2022Article
- Current State of Metabolomics Research in Meat Quality Analysis and Authentication.Foods (Basel, Switzerland) · 2021Review
- Tick-Tock Consider the Clock: The Influence of Circadian and External Cycles on Time of Day Variation in the Human Metabolome-A Review.Metabolites · 2021Review
- Normalization methods for reducing interbatch effect without quality control samples in liquid chromatography-mass spectrometry-based studies.Analytical and bioanalytical chemistry · 2021Article
- Osmolality-based normalization enhances statistical discrimination of untargeted metabolomic urine analysis: results from a comparative study.Metabolomics : Official journal of the Metabolomic Society · 2021Article
- Urinary 8-OxoGsn as a Potential Indicator of Mild Cognitive Impairment in Frail Patients With Cardiovascular Disease.Frontiers in aging neuroscience · 2021Article
- Metabolomics Data Treatment: Basic Directions of the Full Process.Advances in experimental medicine and biology · 2021Article
- Towards Standardization of Data Normalization Strategies to Improve Urinary Metabolomics Studies by GC×GC-TOFMS.Metabolites · 2020Article
- Profiling of Metabolomic Changes in Plasma and Urine of Pigs Caused by Illegal Administration of Testosterone Esters.Metabolites · 2020Article
- Untargeted Metabolic Profiling of Cat Urine and Plasma in Hypertension.Journal of veterinary internal medicineArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
Human urine recently became a popular medium for metabolomics biomarker discovery because its collection is non-invasive. Sometimes renal dilution of urine can be problematic in this type of urinary biomarker analysis. Currently, various normalization techniques such as creatinine ratio, osmolality, specific gravity, dry mass, urine volume, and area under the curve are used to account for the renal dilution. However, these normalization techniques have their own drawbacks. In this project, mass spectrometry-based urinary metabolomic data obtained from prostate cancer (n = 56), bladder cancer (n = 57) and control (n = 69) groups were analyzed using statistical normalization techniques. The normalization techniques investigated in this study are Creatinine Ratio, Log Value, Linear Baseline, Cyclic Loess, Quantile, Probabilistic Quotient, Auto Scaling, Pareto Scaling, and Variance Stabilizing Normalization. The appropriate summary statistics for comparison of normalization techniques were created using variances, coefficients of variation, and boxplots. For each normalization technique, a principal component analysis was performed to identify clusters based on cancer type. In addition, hypothesis tests were conducted to determine if the normalized biomarkers could be used to differentiate between the cancer types. The results indicate that the determination of statistical significance can be dependent upon which normalization method is utilized. Therefore, careful consideration should go into choosing an appropriate normalization technique as no method had universally superior performance.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.