Research article
Comparative analysis of missing value imputation methods to improve clustering and interpretation of microarray experiments
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* Corresponding author: Alexandre G de Brevern alexandre.debrevern@univ-paris-diderot.fr
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
BMC Genomics 2010, 11:15 doi:10.1186/1471-2164-11-15
Published: 7 January 2010Additional files
Additional file 1:
Dataset details.
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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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