ArticleBriefings in bioinformatics2017
Effects of imputation on correlation: implications for analysis of mass spectrometry data from multiple biological matrices.
Article in Briefings in bioinformatics, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
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Who cites it
23 citing papers in PubMed.
- Algorithms and tools for data-driven omics integration to achieve multilayer biological insights: a narrative review.Journal of translational medicine · 2025Review
- To Impute or Not To Impute in Untargeted Metabolomics─That is the Compositional Question.Journal of the American Society for Mass Spectrometry · 2025Article
- Predicting Biochemical and Physiological Parameters: Deep Learning from IgG Glycome Composition.International journal of molecular sciences · 2024Article
- Seminar: Scalable Preprocessing Tools for Exposomic Data Analysis.Environmental health perspectives · 2023Article
- Challenges and Opportunities for Single-cell Computational Proteomics.Molecular & cellular proteomics : MCP · 2023Review
- Machine learning algorithm-generated and multi-center validated melanoma prognostic signature with inspiration for treatment management.Cancer immunology, immunotherapy : CII · 2023Article
- Imputation of Missing Values for Multi-Biospecimen Metabolomics Studies: Bias and Effects on Statistical Validity.Metabolites · 2022Article
- Optimization of Imputation Strategies for High-Resolution Gas Chromatography-Mass Spectrometry (HR GC-MS) Metabolomics Data.Metabolites · 2022Article
- A multiple phenotype imputation method for genetic diversity and core collection in Taiwanese vegetable soybean.Frontiers in plant science · 2022Article
- Connecting the dots: The boons and banes of network modeling.Patterns (New York, N.Y.) · 2021Review
- A MicroRNA Expression Signature as Prognostic Marker for Oropharyngeal Squamous Cell Carcinoma.Journal of the National Cancer Institute · 2021Article
- DNA methylation of insulin-like growth factor 2 and H19 cluster in cord blood and prenatal air pollution exposure to fine particulate matter.Environmental health : a global access science source · 2020Article
- Metabolomics and Multi-Omics Integration: A Survey of Computational Methods and Resources.Metabolites · 2020Review
- Toward a Standardized Strategy of Clinical Metabolomics for the Advancement of Precision Medicine.Metabolites · 2020Review
- Random forest-based imputation outperforms other methods for imputing LC-MS metabolomics data: a comparative study.BMC bioinformatics · 2019Article
- The Search for Clinically Useful Biomarkers of Complex Disease: A Data Analysis Perspective.Metabolites · 2019Article
- Characterization of missing values in untargeted MS-based metabolomics data and evaluation of missing data handling strategies.Metabolomics : Official journal of the Metabolomic Society · 2018Article
- Missing value imputation for LC-MS metabolomics data by incorporating metabolic network and adduct ion relations.Bioinformatics (Oxford, England) · 2018Article
- Use of Metabolomics in Improving Assessment of Dietary Intake.Clinical chemistry · 2018Review
- Metabolomic profiles of plasma, exhaled breath condensate, and saliva are correlated with potential for air toxics detection.Journal of breath research · 2017Article
Corrections and comments
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5 authors.
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Abstract
With expanded access to, and decreased costs of, mass spectrometry, investigators are collecting and analyzing multiple biological matrices from the same subject such as serum, plasma, tissue and urine to enhance biomarker discoveries, understanding of disease processes and identification of therapeutic targets. Commonly, each biological matrix is analyzed separately, but multivariate methods such as MANOVAs that combine information from multiple biological matrices are potentially more powerful. However, mass spectrometric data typically contain large amounts of missing values, and imputation is often used to create complete data sets for analysis. The effects of imputation on multiple biological matrix analyses have not been studied. We investigated the effects of seven imputation methods (half minimum substitution, mean substitution, k-nearest neighbors, local least squares regression, Bayesian principal components analysis, singular value decomposition and random forest), on the within-subject correlation of compounds between biological matrices and its consequences on MANOVA results. Through analysis of three real omics data sets and simulation studies, we found the amount of missing data and imputation method to substantially change the between-matrix correlation structure. The magnitude of the correlations was generally reduced in imputed data sets, and this effect increased with the amount of missing data. Significant results from MANOVA testing also were substantially affected. In particular, the number of false positives increased with the level of missing data for all imputation methods. No one imputation method was universally the best, but the simple substitution methods (Half Minimum and Mean) consistently performed poorly.
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