Multiple imputations applied to the DREAM3 phosphoproteomics challenge: a winning strategy.

Détails

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Etat: Public
Version: Final published version
Licence: CC BY 4.0
ID Serval
serval:BIB_2D837D79DCAA
Type
Article: article d'un périodique ou d'un magazine.
Collection
Publications
Titre
Multiple imputations applied to the DREAM3 phosphoproteomics challenge: a winning strategy.
Périodique
PLoS One
Auteur(s)
Guex N., Migliavacca E., Xenarios I.
ISSN
1932-6203 (Electronic)
ISSN-L
1932-6203
Statut éditorial
Publié
Date de publication
2010
Peer-reviewed
Oui
Volume
5
Numéro
1
Pages
e8012
Langue
anglais
Résumé
DREAM is an initiative that allows researchers to assess how well their methods or approaches can describe and predict networks of interacting molecules [1]. Each year, recently acquired datasets are released to predictors ahead of publication. Researchers typically have about three months to predict the masked data or network of interactions, using any predictive method. Predictions are assessed prior to an annual conference where the best predictions are unveiled and discussed. Here we present the strategy we used to make a winning prediction for the DREAM3 phosphoproteomics challenge. We used Amelia II, a multiple imputation software method developed by Gary King, James Honaker and Matthew Blackwell[2] in the context of social sciences to predict the 476 out of 4624 measurements that had been masked for the challenge. To chose the best possible multiple imputation parameters to apply for the challenge, we evaluated how transforming the data and varying the imputation parameters affected the ability to predict additionally masked data. We discuss the accuracy of our findings and show that multiple imputations applied to this dataset is a powerful method to accurately estimate the missing data. We postulate that multiple imputations methods might become an integral part of experimental design as a mean to achieve cost savings in experimental design or to increase the quantity of samples that could be handled for a given cost.
Mots-clé
Phosphoproteins/metabolism, Principal Component Analysis, Proteomics, Software
Pubmed
Web of science
Open Access
Oui
Création de la notice
12/11/2010 18:37
Dernière modification de la notice
20/08/2019 13:12
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