ArticleBioinformatics (Oxford, England)2021
RobNorm: model-based robust normalization method for labeled quantitative mass spectrometry proteomics data.
Article in Bioinformatics (Oxford, England), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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13 citing papers in PubMed.
- A First-Trimester Serum Proteomic Signature for Early Prediction of Preeclampsia: Integrated Untargeted and Targeted Mass Spectrometry with Machine Learning.Life (Basel, Switzerland) · 2026Article
- Integrated Clinical and Molecular Profiling of Fetal Growth Disorders in the First Trimester.International journal of molecular sciences · 2026Article
- Machine Learning and Blood-Targeted Proteomics Enable Early Prediction and Etiological Discrimination of Hypertensive Pregnancy Disorders.International journal of molecular sciences · 2026Article
- Multiplexed Quantification of First-Trimester Serum Biomarkers in Healthy Pregnancy.International journal of molecular sciences · 2025Article
- Privacy-preserving multicenter differential protein abundance analysis with FedProt.Nature computational science · 2025Article
- Evaluation of normalization strategies for mass spectrometry-based multi-omics datasets.Metabolomics : Official journal of the Metabolomic Society · 2025Article
- Systematic evaluation of normalization approaches in tandem mass tag and label-free protein quantification data using PRONE.Briefings in bioinformatics · 2025Article
- Comprehensive Overview of Bottom-Up Proteomics Using Mass Spectrometry.ACS measurement science au · 2024Review
- Article
- Deep Proteomics Network and Machine Learning Analysis of Human Cerebrospinal Fluid in Japanese Encephalitis Virus Infection.Journal of proteome research · 2023Article
- Accounting for multiple imputation-induced variability for differential analysis in mass spectrometry-based label-free quantitative proteomics.PLoS computational biology · 2022Article
- Diagnostics and correction of batch effects in large-scale proteomic studies: a tutorial.Molecular systems biology · 2021Review
- Article
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7 authors.
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Abstract
motivationData normalization is an important step in processing proteomics data generated in mass spectrometry experiments, which aims to reduce sample-level variation and facilitate comparisons of samples. Previously published methods for normalization primarily depend on the assumption that the distribution of protein expression is similar across all samples. However, this assumption fails when the protein expression data is generated from heterogenous samples, such as from various tissue types. This led us to develop a novel data-driven method for improved normalization to correct the systematic bias meanwhile maintaining underlying biological heterogeneity.
resultsTo robustly correct the systematic bias, we used the density-power-weight method to down-weigh outliers and extended the one-dimensional robust fitting method described in the previous work to our structured data. We then constructed a robustness criterion and developed a new normalization algorithm, called RobNorm.In simulation studies and analysis of real data from the genotype-tissue expression project, we compared and evaluated the performance of RobNorm against other normalization methods. We found that the RobNorm approach exhibits the greatest reduction in systematic bias while maintaining across-tissue variation, especially for datasets from highly heterogeneous samples. AVAILABILITYAND IMPLEMENTATION: https://github.com/mwgrassgreen/RobNorm. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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