ArticleBriefings in bioinformatics2022
Comparison of imputation and imputation-free methods for statistical analysis of mass spectrometry data with missing data.
Article in Briefings in bioinformatics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed.
- An imputation method for single-cell RNA sequencing data based on a parallel gated recurrent unit neural network.Quantitative biology (Beijing, China) · 2026Article
- Integrated Physiological and Multi-Omics Analysis Reveals Selenium-Mediated Drought Tolerance inInternational journal of molecular sciences · 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
- Overcoming missing data in spatial metabolomics with machine learning imputation to accelerate downstream discovery.iScience · 2026Article
- Missing data imputation of climate time series: A review.MethodsX · 2025Review
- Genetic architecture and analysis practices of circulating metabolites in the NHLBI Trans-Omics for Precision Medicine Program.American journal of human genetics · 2025Article
- Quantitative Assessment of Biological Dynamics with Aggregate Data.Bulletin of mathematical biology · 2025Article
- De-MSI: A Deep Learning-Based Data Denoising Method to Enhance Mass Spectrometry Imaging by Leveraging the Chemical Prior Knowledge.Analytical chemistry · 2025Article
- Evaluation of imputation and imputation-free strategies for differential abundance analysis in metaproteomics data.Briefings in bioinformatics · 2025Article
- A Lipidomics Approach to Determine the Role of Lipids and Its Crosstalk with Autophagy in Lung Cancer Metastasis.Methods in molecular biology (Clifton, N.J.) · 2025Article
- Comprehensive Evaluation of Advanced Imputation Methods for Proteomic Data Acquired via the Label-Free Approach.International journal of molecular sciences · 2024Article
- Tutorial on survival modeling with applications to omics data.Bioinformatics (Oxford, England) · 2024Article
- Exploiting nearest neighbor data and fuzzy membership function to address missing values in classification.PeerJ. Computer science · 2024Article
- Imputation of Missing Values for Multi-Biospecimen Metabolomics Studies: Bias and Effects on Statistical Validity.Metabolites · 2022Article
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4 authors.
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Abstract
Missing values are common in high-throughput mass spectrometry data. Two strategies are available to address missing values: (i) eliminate or impute the missing values and apply statistical methods that require complete data and (ii) use statistical methods that specifically account for missing values without imputation (imputation-free methods). This study reviews the effect of sample size and percentage of missing values on statistical inference for multiple methods under these two strategies. With increasing missingness, the ability of imputation and imputation-free methods to identify differentially and non-differentially regulated compounds in a two-group comparison study declined. Random forest and k-nearest neighbor imputation combined with a Wilcoxon test performed well in statistical testing for up to 50% missingness with little bias in estimating the effect size. Quantile regression imputation accompanied with a Wilcoxon test also had good statistical testing outcomes but substantially distorted the difference in means between groups. None of the imputation-free methods performed consistently better for statistical testing than imputation methods.
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