ArticleBriefings in bioinformatics2018
A systematic evaluation of normalization methods in quantitative label-free proteomics.
Article in Briefings in bioinformatics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 231 papers, 1 of them a synthesis that pooled it.
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
231 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Reproducibility Crossroads: Impact of Statistical Choices on Proteomics Functional Enrichment.International journal of molecular sciences · 2025Pooled it
- Proteomic Analysis of Valsartan for Attenuating Disease Evolution in Early Sarcomeric Hypertrophic Cardiomyopathy (VANISH) Clinical Trial.Circulation. Heart failure · 2025Trial
- Selective Influence of Hemp Fiber Ingestion on Post-Exercise Gut Permeability: A Metabolomics-Based Analysis.Nutrients · 2025Trial
- Trial
- The Case for Standardized Normalization of Tear Fluid Proteomics.Ophthalmology science · 2026Article
- Multi-omics integration of proteomics and metabolomics in pediatric health and disease.Communications medicine · 2026Review
- Review
- Protein Variability Patterns in Ovarian Serous Carcinoma.International journal of molecular sciences · 2026Article
- Multi-omics-driven precision medicine.iMeta · 2026Review
- Spatial Deconstruction of the Plasma Membrane.Journal of the American Chemical Society · 2026Article
- GlycoDiveR: A Modular R Framework to Analyze and Visualize Highly Dimensional Glycoproteomics Data.ACS measurement science au · 2026Article
- Article
- Towards a proteomic plasma biomarker panel for diagnosing vasculitis remission.Nature communications · 2026Article
- Serum Proteomic Profiling Implicates a Dysregulated Neurohormonal-Inflammatory Axis in Post-Fontan Sinus Tachycardia.Journal of the American Heart Association · 2026Article
- msBayesImpute as a versatile framework for addressing missing values in biomedical mass spectrometry proteomics data.Communications chemistry · 2026Article
- MPOInt: An Epigenetics-Driven Workflow for Integrative Multi-Level Proteomics.Journal of proteome research · 2026Article
- A sequential MAP kinase cascade regulates mechanical signalling.Nature communications · 2026Article
- Plasma proteomic profiles of lung volume-based phenotypes in tobacco-exposed individuals without spirometric chronic obstructive pulmonary disease.Annals of the American Thoracic Society · 2026Observational
- Time-Resolved Label-Free Proteomics of SHK-1 Cells AfterInternational journal of molecular sciences · 2026Article
- A novelApplied and environmental microbiology · 2026Article
171 more citing papers are in PubMed but not listed here.
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
To date, mass spectrometry (MS) data remain inherently biased as a result of reasons ranging from sample handling to differences caused by the instrumentation. Normalization is the process that aims to account for the bias and make samples more comparable. The selection of a proper normalization method is a pivotal task for the reliability of the downstream analysis and results. Many normalization methods commonly used in proteomics have been adapted from the DNA microarray techniques. Previous studies comparing normalization methods in proteomics have focused mainly on intragroup variation. In this study, several popular and widely used normalization methods representing different strategies in normalization are evaluated using three spike-in and one experimental mouse label-free proteomic data sets. The normalization methods are evaluated in terms of their ability to reduce variation between technical replicates, their effect on differential expression analysis and their effect on the estimation of logarithmic fold changes. Additionally, we examined whether normalizing the whole data globally or in segments for the differential expression analysis has an effect on the performance of the normalization methods. We found that variance stabilization normalization (Vsn) reduced variation the most between technical replicates in all examined data sets. Vsn also performed consistently well in the differential expression analysis. Linear regression normalization and local regression normalization performed also systematically well. Finally, we discuss the choice of a normalization method and some qualities of a suitable normalization method in the light of the results of our evaluation.
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.