ArticleScientific reports2022
Comparative assessment and novel strategy on methods for imputing proteomics data.
Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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Who cites it
20 citing papers in PubMed.
- Aggrecan and polymeric immunoglobulin receptor in extracellular vesicles of patients with seropositive rheumatoid arthritis.Journal of translational autoimmunity · 2026Article
- TRPA1 deficiency alters brown adipose tissue proteome and thermogenic gene expression during cold exposure in mice.Biochemistry and biophysics reports · 2026Article
- In vitro Host-Parasite Models Combined With Proteomics Reveal Cell Type-Specific Responses of Early Migrating Fasciola hepatica Juveniles.Molecular & cellular proteomics : MCP · 2026Article
- Article
- Computational and Statistical Framework for Quantitative Proteomics Analysis.Methods in molecular biology (Clifton, N.J.) · 2026Article
- Impact of clinical factors and season on inflammatory cytokines in biologic-treated and untreated asthma.Respiratory research · 2025Article
- AUGMENTED DOUBLY ROBUST POST-IMPUTATION INFERENCE FOR PROTEOMIC DATA.The annals of applied statistics · 2025Article
- AUGMENTED DOUBLY ROBUST POST-IMPUTATION INFERENCE FOR PROTEOMIC DATA.bioRxiv : the preprint server for biology · 2025Article
- Quantitative Top-down Proteomics Revealed Kinase Inhibitor-Induced Proteoform-Level Changes in Cancer Cells.Journal of proteome research · 2025Article
- Optimizing imputation strategies for mass spectrometry-based proteomics considering intensity and missing value rates.Computational and structural biotechnology journal · 2025Article
- iDDN: determining trans-omics network structure and rewiring with integrative differential dependency networks.Bioinformatics advances · 2025Article
- Embracing the informative missingness and silent gene in analyzing biologically diverse samples.Scientific reports · 2024Article
- Missing Values in Longitudinal Proteome Dynamics Studies: Making a Case for Data Multiple Imputation.Journal of proteome research · 2024Article
- PEPerMINT: peptide abundance imputation in mass spectrometry-based proteomics using graph neural networks.Bioinformatics (Oxford, England) · 2024Article
- LipidSig 2.0: integrating lipid characteristic insights into advanced lipidomics data analysis.Nucleic acids research · 2024Article
- ABDS: a bioinformatics tool suite for analyzing biologically diverse samples.Research square · 2024Article
- Neither random nor censored: estimating intensity-dependent probabilities for missing values in label-free proteomics.Bioinformatics (Oxford, England) · 2023Article
- Challenges and Opportunities for Single-cell Computational Proteomics.Molecular & cellular proteomics : MCP · 2023Review
- BIRCH: An Automated Workflow for Evaluation, Correction, and Visualization of Batch Effect in Bottom-Up Mass Spectrometry-Based Proteomics Data.Journal of proteome research · 2023Article
- The community ecology perspective of omics data.Microbiome · 2022Article
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11 authors.
Funding
Abstract
Missing values are a major issue in quantitative proteomics analysis. While many methods have been developed for imputing missing values in high-throughput proteomics data, a comparative assessment of imputation accuracy remains inconclusive, mainly because mechanisms contributing to true missing values are complex and existing evaluation methodologies are imperfect. Moreover, few studies have provided an outlook of future methodological development. We first re-evaluate the performance of eight representative methods targeting three typical missing mechanisms. These methods are compared on both simulated and masked missing values embedded within real proteomics datasets, and performance is evaluated using three quantitative measures. We then introduce fused regularization matrix factorization, a low-rank global matrix factorization framework, capable of integrating local similarity derived from additional data types. We also explore a biologically-inspired latent variable modeling strategy-convex analysis of mixtures-for missing value imputation and present preliminary experimental results. While some winners emerged from our comparative assessment, the evaluation is intrinsically imperfect because performance is evaluated indirectly on artificial missing or masked values not authentic missing values. Nevertheless, we show that our fused regularization matrix factorization provides a novel incorporation of external and local information, and the exploratory implementation of convex analysis of mixtures presents a biologically plausible new approach.
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