ArticleMolecular & cellular proteomics : MCP2009
Development and evaluation of normalization methods for label-free relative quantification of endogenous peptides.
Article in Molecular & cellular proteomics : MCP, 2009. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 39 papers.
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39 citing papers in PubMed.
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- Protein-level batch-effect correction enhances robustness in MS-based proteomics.Nature communications · 2025Article
- Systematic evaluation of normalization approaches in tandem mass tag and label-free protein quantification data using PRONE.Briefings in bioinformatics · 2025Article
- Tumor-Promoted Changes in Pediatric Brain Histology Can Be Distinguished from Normal Parenchyma by Desorption Electrospray Ionization Mass Spectrometry Imaging.Biomedicines · 2024Article
- Tumor specimen cold ischemia time impacts molecular cancer drug target discovery.Cell death & disease · 2024Article
- Comprehensive Overview of Bottom-Up Proteomics Using Mass Spectrometry.ACS measurement science au · 2024Review
- Two Different Strategies for Stabilization of Brain Tissue and Extraction of Neuropeptides.Methods in molecular biology (Clifton, N.J.) · 2024Article
- Label-Free Quantitation of Endogenous Peptides.Methods in molecular biology (Clifton, N.J.) · 2024Article
- Article
- Evaluation of Normalization Approaches for Quantitative Analysis of Bile Acids in Human Feces.Metabolites · 2022Article
- RobNorm: model-based robust normalization method for labeled quantitative mass spectrometry proteomics data.Bioinformatics (Oxford, England) · 2021Article
- ANPELA: analysis and performance assessment of the label-free quantification workflow for metaproteomic studies.Briefings in bioinformatics · 2020Article
- MS1 ion current-based quantitative proteomics: A promising solution for reliable analysis of large biological cohorts.Mass spectrometry reviews · 2019Review
- What is Normalization? The Strategies Employed in Top-Down and Bottom-Up Proteome Analysis Workflows.Proteomes · 2019Review
- Clinical biomarker discovery by SWATH-MS based label-free quantitative proteomics: impact of criteria for identification of differentiators and data normalization method.Journal of translational medicine · 2019Article
- Assessment of Sample Preparation Bias in Mass Spectrometry-Based Proteomics.Analytical chemistry · 2018Article
- A systematic evaluation of normalization methods in quantitative label-free proteomics.Briefings in bioinformatics · 2018Article
- Whole Blood Storage in CPDA1 Blood Bags Alters Erythrocyte Membrane Proteome.Oxidative medicine and cellular longevity · 2018Article
- Performance Evaluation and Online Realization of Data-driven Normalization Methods Used in LC/MS based Untargeted Metabolomics Analysis.Scientific reports · 2016Article
- Preprocessing and Analysis of LC-MS-Based Proteomic Data.Methods in molecular biology (Clifton, N.J.) · 2016Article
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6 authors.
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Abstract
The performances of 10 different normalization methods on data of endogenous brain peptides produced with label-free nano-LC-MS were evaluated. Data sets originating from three different species (mouse, rat, and Japanese quail), each consisting of 35-45 individual LC-MS analyses, were used in the study. Each sample set contained both technical and biological replicates, and the LC-MS analyses were performed in a randomized block fashion. Peptides in all three data sets were found to display LC-MS analysis order-dependent bias. Global normalization methods will only to some extent correct this type of bias. Only the novel normalization procedure RegrRun (linear regression followed by analysis order normalization) corrected for this type of bias. The RegrRun procedure performed the best of the normalization methods tested and decreased the median S.D. by 43% on average compared with raw data. This method also produced the smallest fraction of peptides with interblock differences while producing the largest fraction of differentially expressed peaks between treatment groups in all three data sets. Linear regression normalization (Regr) performed second best and decreased median S.D. by 38% on average compared with raw data. All other examined methods reduced median S.D. by 20-30% on average compared with raw data.
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