ArticleScientific reports2021
A comparative study of evaluating missing value imputation methods in label-free proteomics.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 92 papers.
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92 citing papers in PubMed, 164 citations in OpenAlex.
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- Cross-disease LC-MS/MS plasma proteomics identifies reproducible shared and disease-enriched biomarker signatures in neurodegenerative disorders.Acta neuropathologica communications · 2026Article
- msBayesImpute as a versatile framework for addressing missing values in biomedical mass spectrometry proteomics data.Communications chemistry · 2026Article
- Multidimensional proteomics and explainable AI feature selection identify cross-platform lung cancer molecular signature in blood plasma.Communications medicine · 2026Article
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- PeptideVisualizer: A Novel Software Solution for PROTOMAP Analysis.Journal of proteome research · 2026Article
- BioProEV: A Bioinformatics Pipeline for Biologically-Relevant Handling of Missing Values in the Analysis of Extracellular Vesicles by Mass Spectrometry.Journal of extracellular biology · 2026Article
- TheJournal of fungi (Basel, Switzerland) · 2026Article
- Overcoming missing data in spatial metabolomics with machine learning imputation to accelerate downstream discovery.iScience · 2026Article
- Enabling cross-indication protein expression analysis using a curated pan-cancer dataset and a tailored workflow.Scientific reports · 2026Article
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- Spatial protein expression patterns across pathologically-associated fibers revealed molecular specialization in inclusion body myositis.Cell communication and signaling : CCS · 2026Article
- Interleukin-6 restricts pre-thymic T cell lineage commitment of progenitors driving loss of SIV control.bioRxiv : the preprint server for biology · 2026Article
- Mass Spectrometry Proteomics: A Key to Faster Drug Discovery.Journal of medicinal chemistry · 2026Review
32 more citing papers are in PubMed but not listed here.
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7 authors at 2 institutions in 1 country.
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Abstract
The presence of missing values (MVs) in label-free quantitative proteomics greatly reduces the completeness of data. Imputation has been widely utilized to handle MVs, and selection of the proper method is critical for the accuracy and reliability of imputation. Here we present a comparative study that evaluates the performance of seven popular imputation methods with a large-scale benchmark dataset and an immune cell dataset. Simulated MVs were incorporated into the complete part of each dataset with different combinations of MV rates and missing not at random (MNAR) rates. Normalized root mean square error (NRMSE) was applied to evaluate the accuracy of protein abundances and intergroup protein ratios after imputation. Detection of true positives (TPs) and false altered-protein discovery rate (FADR) between groups were also compared using the benchmark dataset. Furthermore, the accuracy of handling real MVs was assessed by comparing enriched pathways and signature genes of cell activation after imputing the immune cell dataset. We observed that the accuracy of imputation is primarily affected by the MNAR rate rather than the MV rate, and downstream analysis can be largely impacted by the selection of imputation methods. A random forest-based imputation method consistently outperformed other popular methods by achieving the lowest NRMSE, high amount of TPs with the average FADR < 5%, and the best detection of relevant pathways and signature genes, highlighting it as the most suitable method for label-free proteomics.
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