ArticleBMC bioinformatics2007
Robust imputation method for missing values in microarray data.
Article in BMC bioinformatics, 2007. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Random forest-based imputation outperforms other methods for imputing LC-MS metabolomics data: a comparative study.BMC bioinformatics · 2019Article
- Computational identification and analysis of early diagnostic biomarkers for kidney cancer.Journal of human genetics · 2019Article
- Imputation of Gene Expression Data in Blood Cancer and Its Significance in Inferring Biological Pathways.Frontiers in oncology · 2019Article
- Characterizing the effects of missing data and evaluating imputation methods for chemical prioritization applications using ToxPi.BioData mining · 2018Article
- A feature selection method based on multiple kernel learning with expression profiles of different types.BioData mining · 2017Article
- Integrated approaches to miRNAs target definition: time-series analysis in an osteosarcoma differentiative model.BMC medical genomics · 2015Article
- Application of quantile regression to recent genetic and -omic studies.Human genetics · 2014Review
- Investigating the effects of imputation methods for modelling gene networks using a dynamic bayesian network from gene expression data.The Malaysian journal of medical sciences : MJMS · 2014Article
- Incorporating Nonlinear Relationships in Microarray Missing Value Imputation.IEEE/ACM transactions on computational biology and bioinformaticsArticle
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Authors and funding
3 authors.
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Abstract
backgroundWhen analyzing microarray gene expression data, missing values are often encountered. Most multivariate statistical methods proposed for microarray data analysis cannot be applied when the data have missing values. Numerous imputation algorithms have been proposed to estimate the missing values. In this study, we develop a robust least squares estimation with principal components (RLSP) method by extending the local least square imputation (LLSimpute) method. The basic idea of our method is to employ quantile regression to estimate the missing values, using the estimated principal components of a selected set of similar genes.
resultsUsing the normalized root mean squares error, the performance of the proposed method was evaluated and compared with other previously proposed imputation methods. The proposed RLSP method clearly outperformed the weighted k-nearest neighbors imputation (kNNimpute) method and LLSimpute method, and showed competitive results with Bayesian principal component analysis (BPCA) method.
conclusionAdapting the principal components of the selected genes and employing the quantile regression model improved the robustness and accuracy of missing value imputation. Thus, the proposed RLSP method is, according to our empirical studies, more robust and accurate than the widely used kNNimpute and LLSimpute methods.
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