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(Reference retrieved automatically from Web of Science through information on FAPESP grant and its corresponding number as mentioned in the publication by the authors.)

Impact of missing data imputation methods on gene expression clustering and classification

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de Souto, Marcilio C. P. [1] ; Jaskowiak, Pablo A. [2] ; Costa, Ivan G. [3, 4]
Total Authors: 3
[1] Univ Orleans, INSA Ctr Val Loire, LIFO EA 4022, Orleans - France
[2] Univ Sao Paulo, Inst Math & Comp Sci, Sao Carlos, SP - Brazil
[3] Univ Fed Pernambuco, Ctr Informat, Recife, PE - Brazil
[4] Rhein Westfal TH Aachen, Sch Med, Inst Biomed Engn, IZKF Computat Biol Res Grp, Aachen - Germany
Total Affiliations: 4
Document type: Journal article
Source: BMC Bioinformatics; v. 16, FEB 26 2015.
Web of Science Citations: 19

Background: Several missing value imputation methods for gene expression data have been proposed in the literature. In the past few years, researchers have been putting a great deal of effort into presenting systematic evaluations of the different imputation algorithms. Initially, most algorithms were assessed with an emphasis on the accuracy of the imputation, using metrics such as the root mean squared error. However, it has become clear that the success of the estimation of the expression value should be evaluated in more practical terms as well. One can consider, for example, the ability of the method to preserve the significant genes in the dataset, or its discriminative/predictive power for classification/clustering purposes. Results and conclusions: We performed a broad analysis of the impact of five well-known missing value imputation methods on three clustering and four classification methods, in the context of 12 cancer gene expression datasets. We employed a statistical framework, for the first time in this field, to assess whether different imputation methods improve the performance of the clustering/classification methods. Our results suggest that the imputation methods evaluated have a minor impact on the classification and downstream clustering analyses. Simple methods such as replacing the missing values by mean or the median values performed as well as more complex strategies. The datasets analyzed in this study are available at (AU)

FAPESP's process: 11/04247-5 - Gene selection and clustering validation in gene expression data
Grantee:Pablo Andretta Jaskowiak
Support type: Scholarships in Brazil - Doctorate