ArticleInternational journal of molecular sciences2021
Multiple Imputation Approaches Applied to the Missing Value Problem in Bottom-Up Proteomics.
Article in International journal of molecular sciences, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers, 1 of them a synthesis that pooled it.
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Who cites it
31 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Prognostic models predicting transition to psychotic disorder using blood-based biomarkers: a systematic review and critical appraisal.Translational psychiatry · 2023Pooled it
- Multi-omics integration of proteomics and metabolomics in pediatric health and disease.Communications medicine · 2026Review
- Proteomics-based clustering for higher risk of venous thromboembolism in patients with nephrotic syndrome.Research and practice in thrombosis and haemostasis · 2026Article
- Structural diversity of lipid A modulates neutrophil proteome and secretome responses.Journal of proteomics · 2026Article
- AI and Machine Learning for Proteomics-Driven Drug Discovery: Methods, Tools, and Best Practices.Current issues in molecular biology · 2026Review
- 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
- Practical Impact of Imputation and Batch-Effect Correction for Proteomics/Peptidomics Differential-Abundance Analysis.Proteomics · 2026Article
- Proteomic remodeling during tumor cell-induced platelet aggregation unveils metastatic drivers in colorectal cancer.Cancer cell international · 2026Article
- Enabling cross-indication protein expression analysis using a curated pan-cancer dataset and a tailored workflow.Scientific reports · 2026Article
- Hat1 Orchestrates Heterochromatin Inheritance by Regulating Localization of H3K9 Methyltransferases.bioRxiv : the preprint server for biology · 2026Article
- Mass Spectrometry Proteomics: A Key to Faster Drug Discovery.Journal of medicinal chemistry · 2026Review
- Plasma Metabolomic and Proteomic Signatures of Blood Pressure Management After Bariatric Surgery Among Adolescents.Hypertension (Dallas, Tex. : 1979) · 2025Article
- Designing a Comparative Proteomics Experiment: Retention-Time Alignment and Imputation Algorithms Affect Statistical Comparisons between Samples.Journal of proteome research · 2025Article
- The proteomic landscape of diffuse midline glioma highlights the therapeutic potential of non-histone protein methyltransferases.Neuro-oncology · 2025Article
- Analysis of FAIMS for the study of affinity-purified protein complexes using the orbitrap ascend tribrid mass spectrometer.Molecular omics · 2025Article
- Plasma Proteomic Profiling of a Group of Anxious Dogs by LC-MS/MS: A Case-Control Study.Proteomics. Clinical applications · 2025Article
- Algorithms and tools for data-driven omics integration to achieve multilayer biological insights: a narrative review.Journal of translational medicine · 2025Review
- Assessing the impact of batch effect associated missing values on downstream analysis in high-throughput biomedical data.Briefings in bioinformatics · 2025Article
- Tau filaments are tethered within brain extracellular vesicles in Alzheimer's disease.Nature neuroscience · 2025Article
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2 authors.
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Abstract
Analysis of differential abundance in proteomics data sets requires careful application of missing value imputation. Missing abundance values widely vary when performing comparisons across different sample treatments. For example, one would expect a consistent rate of "missing at random" (MAR) across batches of samples and varying rates of "missing not at random" (MNAR) depending on the inherent difference in sample treatments within the study. The missing value imputation strategy must thus be selected that best accounts for both MAR and MNAR simultaneously. Several important issues must be considered when deciding the appropriate missing value imputation strategy: (1) when it is appropriate to impute data; (2) how to choose a method that reflects the combinatorial manner of MAR and MNAR that occurs in an experiment. This paper provides an evaluation of missing value imputation strategies used in proteomics and presents a case for the use of hybrid left-censored missing value imputation approaches that can handle the MNAR problem common to proteomics data.
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