ArticleBioinformatics (Oxford, England)2011
Biological impact of missing-value imputation on downstream analyses of gene expression profiles.
Article in Bioinformatics (Oxford, England), 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers.
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Who cites it
31 citing papers in PubMed.
- SoftHybrid: A Hybrid Imputation Algorithm Optimized for Single-Cell Proteomics Data.Journal of proteome research · 2026Article
- A Hands-On Introduction to Data Analytics for Biomedical Research.Function (Oxford, England) · 2025Review
- Comprehensive Evaluation of Advanced Imputation Methods for Proteomic Data Acquired via the Label-Free Approach.International journal of molecular sciences · 2024Article
- Wise Roles and Future Visionary Endeavors of Current Emperor: Advancing Dynamic Methods for Longitudinal Microbiome Meta-Omics Data in Personalized and Precision Medicine.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2024Review
- Censored Least Squares for Imputing Missing Values in PARAFAC Tensor Factorization.bioRxiv : the preprint server for biology · 2024Article
- Machine learning integrative approaches to advance computational immunology.Genome medicine · 2024Review
- Article
- Evaluation of different approaches for missing data imputation on features associated to genomic data.BioData mining · 2021Article
- Bioinformatic Analysis of Temporal and Spatial Proteome Alternations During Infections.Frontiers in genetics · 2021Review
- A flexible, interpretable, and accurate approach for imputing the expression of unmeasured genes.Nucleic acids research · 2020Article
- Imputing missing RNA-sequencing data from DNA methylation by using a transfer learning-based neural network.GigaScience · 2020Article
- BayesMetab: treatment of missing values in metabolomic studies using a Bayesian modeling approach.BMC bioinformatics · 2019Article
- Article
- Bayesian integrative model for multi-omics data with missingness.Bioinformatics (Oxford, England) · 2018Article
- Classifying Incomplete Gene-Expression Data: Ensemble Learning with Non-Pre-Imputation Feature Filtering and Best-First Search Technique.International journal of molecular sciences · 2018Article
- Distribution based nearest neighbor imputation for truncated high dimensional data with applications to pre-clinical and clinical metabolomics studies.BMC bioinformatics · 2017Article
- MVIAeval: a web tool for comprehensively evaluating the performance of a new missing value imputation algorithm.BMC bioinformatics · 2017Article
- Comparative analysis of codon usage bias in Crenarchaea and Euryarchaea genome reveals differential preference of synonymous codons to encode highly expressed ribosomal and RNA polymerase proteins.Journal of genetics · 2016Article
- Use of multiple time points to model parotid differentiation.Genomics data · 2015Article
- Selected Reaction Monitoring Mass Spectrometry for Absolute Protein Quantification.Journal of visualized experiments : JoVE · 2015Article
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4 authors.
Funding
Abstract
motivationMicroarray experiments frequently produce multiple missing values (MVs) due to flaws such as dust, scratches, insufficient resolution or hybridization errors on the chips. Unfortunately, many downstream algorithms require a complete data matrix. The motivation of this work is to determine the impact of MV imputation on downstream analysis, and whether ranking of imputation methods by imputation accuracy correlates well with the biological impact of the imputation.
methodsUsing eight datasets for differential expression (DE) and classification analysis and eight datasets for gene clustering, we demonstrate the biological impact of missing-value imputation on statistical downstream analyses, including three commonly employed DE methods, four classifiers and three gene-clustering methods. Correlation between the rankings of imputation methods based on three root-mean squared error (RMSE) measures and the rankings based on the downstream analysis methods was used to investigate which RMSE measure was most consistent with the biological impact measures, and which downstream analysis methods were the most sensitive to the choice of imputation procedure.
resultsDE was the most sensitive to the choice of imputation procedure, while classification was the least sensitive and clustering was intermediate between the two. The logged RMSE (LRMSE) measure had the highest correlation with the imputation rankings based on the DE results, indicating that the LRMSE is the best representative surrogate among the three RMSE-based measures. Bayesian principal component analysis and least squares adaptive appeared to be the best performing methods in the empirical downstream evaluation.
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