ArticleBMC bioinformatics2008
Missing value imputation improves clustering and interpretation of gene expression microarray data.
Article in BMC bioinformatics, 2008. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed.
- Missing value replacement in strings and applications.Data mining and knowledge discovery · 2025Article
- A comprehensive survey on computational learning methods for analysis of gene expression data.Frontiers in molecular biosciences · 2022Review
- An introduction to machine learning and analysis of its use in rheumatic diseases.Nature reviews. Rheumatology · 2021Review
- A comparative study of evaluating missing value imputation methods in label-free proteomics.Scientific reports · 2021Article
- A flexible, interpretable, and accurate approach for imputing the expression of unmeasured genes.Nucleic acids research · 2020Article
- A comprehensive evaluation of popular proteomics software workflows for label-free proteome quantification and imputation.Briefings in bioinformatics · 2018Article
- MVIAeval: a web tool for comprehensively evaluating the performance of a new missing value imputation algorithm.BMC bioinformatics · 2017Article
- Review, evaluation, and discussion of the challenges of missing value imputation for mass spectrometry-based label-free global proteomics.Journal of proteome research · 2015Review
- Impact of missing data imputation methods on gene expression clustering and classification.BMC bioinformatics · 2015Article
- Comparing Imputation Procedures for Affymetrix Gene Expression Datasets Using MAQC Datasets.Advances in bioinformatics · 2013Article
- Missing value imputation for microarray data: a comprehensive comparison study and a web tool.BMC systems biology · 2013Article
- Imputing and predicting quantitative genetic interactions in epistatic MAPs.Methods in molecular biology (Clifton, N.J.) · 2011Article
- Prediction and Characterization of Missing Proteomic Data in Desulfovibrio vulgaris.Comparative and functional genomics · 2011Article
- Biological impact of missing-value imputation on downstream analyses of gene expression profiles.Bioinformatics (Oxford, England) · 2011Article
- Comparative analysis of missing value imputation methods to improve clustering and interpretation of microarray experiments.BMC genomics · 2010Article
- Impact of missing value imputation on classification for DNA microarray gene expression data--a model-based study.EURASIP journal on bioinformatics & systems biology · 2009Article
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4 authors.
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Abstract
backgroundMissing values frequently pose problems in gene expression microarray experiments as they can hinder downstream analysis of the datasets. While several missing value imputation approaches are available to the microarray users and new ones are constantly being developed, there is no general consensus on how to choose between the different methods since their performance seems to vary drastically depending on the dataset being used.
resultsWe show that this discrepancy can mostly be attributed to the way in which imputation methods have traditionally been developed and evaluated. By comparing a number of advanced imputation methods on recent microarray datasets, we show that even when there are marked differences in the measurement-level imputation accuracies across the datasets, these differences become negligible when the methods are evaluated in terms of how well they can reproduce the original gene clusters or their biological interpretations. Regardless of the evaluation approach, however, imputation always gave better results than ignoring missing data points or replacing them with zeros or average values, emphasizing the continued importance of using more advanced imputation methods.
conclusionThe results demonstrate that, while missing values are still severely complicating microarray data analysis, their impact on the discovery of biologically meaningful gene groups can - up to a certain degree - be reduced by using readily available and relatively fast imputation methods, such as the Bayesian Principal Components Algorithm (BPCA).
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