ArticleJournal of proteome research2023
Evaluating Proteomics Imputation Methods with Improved Criteria.
Article in Journal of proteome research, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers.
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Who cites it
33 citing papers in PubMed.
- Plasma proteomic signatures of social support and their association with cardiovascular disease and mortality: exploratory analyses in a national cohort study.GeroScience · 2026Article
- GlycoDiveR: A Modular R Framework to Analyze and Visualize Highly Dimensional Glycoproteomics Data.ACS measurement science au · 2026Article
- Genome-wide pQTL mapping in human skin identifies specific genetic regulators and mechanistic links to skin disorders.Nature communications · 2026Article
- CD8 T lymphocytes deploy embryonic cell cycle control mechanisms for rapid cell proliferation.EMBO reports · 2026Article
- Article
- msBayesImpute as a versatile framework for addressing missing values in biomedical mass spectrometry proteomics data.Communications chemistry · 2026Article
- Proteomics reveals spatial and molecular heterogeneities in advanced atherosclerotic carotid artery plaques.Nature cardiovascular research · 2026Article
- Ground Truth-Based Evaluation of False Discovery Rate and Statistical Power in DIA Proteomics.bioRxiv : the preprint server for biology · 2026Article
- Practical Impact of Imputation and Batch-Effect Correction for Proteomics/Peptidomics Differential-Abundance Analysis.Proteomics · 2026Article
- Global impact of germline structural variation on the cancer proteome.Nature communications · 2026Article
- LIN28A-Dependent Kinome and Phosphoproteome Reprogramming Promotes Imatinib Resistance.Molecular & cellular proteomics : MCP · 2026Article
- GlycoDiveR: a modular R framework to analyze and visualize highly dimensional glycoproteomics data.bioRxiv : the preprint server for biology · 2026Article
- The good, the bad, and the ugly: opportunities, challenges, and pitfalls in spatial proteomics modeling.Briefings in bioinformatics · 2026Review
- spammR: an R package designed for analysis and integration of spatial multi-omic measurements.Bioinformatics advances · 2026Article
- QproMS: a web application for label-free proteomic data analysis.Bioinformatics advances · 2026Article
- Proteomics Data Imputation With a Deep Model That Learns From Many Datasets.Molecular & cellular proteomics : MCP · 2025Article
- Bacterial Proteomics and Antibiotic Resistance Identification: Is Single-Cell Analysis a Worthwhile Pursuit?Pathogens (Basel, Switzerland) · 2025Review
- Data Processing and Analysis in Positional Proteomics.Proteomics · 2025Review
- Plasma proteomic signatures of social support and their association with cardiovascular disease and mortality.medRxiv : the preprint server for health sciences · 2025Article
- Laser capture proteomics reveals new candidates for sperm-interacting proteins in the bovine oviduct epithelium.Reproduction (Cambridge, England) · 2025Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
Quantitative measurements produced by tandem mass spectrometry proteomics experiments typically contain a large proportion of missing values. Missing values hinder reproducibility, reduce statistical power, and make it difficult to compare across samples or experiments. Although many methods exist for imputing missing values, in practice, the most commonly used methods are among the worst performing. Furthermore, previous benchmarking studies have focused on relatively simple measurements of error such as the mean-squared error between imputed and held-out values. Here we evaluate the performance of commonly used imputation methods using three practical, "downstream-centric" criteria. These criteria measure the ability to identify differentially expressed peptides, generate new quantitative peptides, and improve the peptide lower limit of quantification. Our evaluation comprises several experiment types and acquisition strategies, including data-dependent and data-independent acquisition. We find that imputation does not necessarily improve the ability to identify differentially expressed peptides but that it can identify new quantitative peptides and improve the peptide lower limit of quantification. We find that MissForest is generally the best performing method per our downstream-centric criteria. We also argue that existing imputation methods do not properly account for the variance of peptide quantifications and highlight the need for methods that do.
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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.