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Open Access Research article

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

Magalie Celton12, Alain Malpertuy3, Gaëlle Lelandais14 and Alexandre G de Brevern14*

Author Affiliations

1 INSERM UMR-S 726, Equipe de Bioinformatique Génomique et Moléculaire (EBGM), DSIMB, Université Paris Diderot - Paris 7, 2, place Jussieu, 75005, France

2 UMR 1083 Sciences pour l'Œnologie INRA, 2 place Viala, 34060 Montpellier cedex 1, France

3 Atragene Informatics, 33-35, Rue Ledru-Rollin 94200 Ivry-sur-Seine, France

4 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

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BMC Genomics 2010, 11:15  doi:10.1186/1471-2164-11-15

Published: 7 January 2010

Additional files

Additional file 1:

Dataset details.

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Open Data

Additional file 2:

RMSE of OS with BPCA imputing method. RMSE value for OS for rate of missing value going from 0.5% to 20% by step of 0.5% with the L dataset.

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Additional file 3:

Extreme values. Distribution of the values observed in OS dataset. The extreme values are highlighted on each size of the histogram.

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Additional file 4:

Comparing clustering algorithms.

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Additional file 5:

Details of CPP and CPPf.

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Open Data