Comparative analysis of missing value imputation methods to improve clustering and interpretation of microarray experiments

BMC Genomics

Research Article

Alain Malpertuy¹  Gaëlle Lelandais²  Alexandre G de Brevern²  Magalie Celton³ 

[1] Atragene Informatics, 33-35, Rue Ledru-Rollin, 94200, Ivry-sur-Seine, France;INSERM UMR-S 726, Equipe de Bioinformatique Génomique et Moléculaire (EBGM), DSIMB, Université Paris Diderot - Paris 7, 2, place Jussieu, 75005, France;INSERM UMR-S 665, DSIMB, Université Paris Diderot - Paris 7, Institut National de Transfusion Sanguine (INTS), 6, rue Alexandre Cabanel, 75739, Paris cedex 15, France;INSERM UMR-S 726, Equipe de Bioinformatique Génomique et Moléculaire (EBGM), DSIMB, Université Paris Diderot - Paris 7, 2, place Jussieu, 75005, France;UMR 1083 Sciences pour l'Œnologie INRA, 2 place Viala, 34060, Montpellier cedex 1, France;

关键词: Root Mean Square Error;    Hierarchical Cluster;    Imputation Method;    Dynamic Time Warping;    Average Root Mean Square Error;   

DOI  :  10.1186/1471-2164-11-15

 received in 2009-09-02, accepted in 2010-01-07,  发布年份 2010

来源: Springer

【 摘 要 】

BackgroundMicroarray technologies produced large amount of data. In a previous study, we have shown the interest of k-Nearest Neighbour approach for restoring the missing gene expression values, and its positive impact of the gene clustering by hierarchical algorithm. Since, numerous replacement methods have been proposed to impute missing values (MVs) for microarray data. In this study, we have evaluated twelve different usable methods, and their influence on the quality of gene clustering. Interestingly we have used several datasets, both kinetic and non kinetic experiments from yeast and human.ResultsWe underline the excellent efficiency of approaches proposed and implemented by Bo and co-workers and especially one based on expected maximization (EM_array). These improvements have been observed also on the imputation of extreme values, the most difficult predictable values. We showed that the imputed MVs have still important effects on the stability of the gene clusters. The improvement on the clustering obtained by hierarchical clustering remains limited and, not sufficient to restore completely the correct gene associations. However, a common tendency can be found between the quality of the imputation method and the gene cluster stability. Even if the comparison between clustering algorithms is a complex task, we observed that k-means approach is more efficient to conserve gene associations.ConclusionsMore than 6.000.000 independent simulations have assessed the quality of 12 imputation methods on five very different biological datasets. Important improvements have so been done since our last study. The EM_array approach constitutes one efficient method for restoring the missing expression gene values, with a lower estimation error level. Nonetheless, the presence of MVs even at a low rate is a major factor of gene cluster instability. Our study highlights the need for a systematic assessment of imputation methods and so of dedicated benchmarks. A noticeable point is the specific influence of some biological dataset.

【 授权许可】

Unknown   
© Celton et al; licensee BioMed Central Ltd. 2010. This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.