Data-based filtering for replicated high-throughput transcriptome sequencing experiments

Andrea Rau 1 Mélina Gallopin 2 Gilles Celeux 2 Florence Jaffrezic 1
2 SELECT - Model selection in statistical learning
Inria Saclay - Ile de France, LMO - Laboratoire de Mathématiques d'Orsay, CNRS - Centre National de la Recherche Scientifique : UMR
Abstract : RNA sequencing is now widely performed to study differential expression among experimental conditions. As tests are performed on a large number of genes, very stringent false discovery rate control is required at the expense of detection power. Ad hoc filtering techniques are regularly used to moderate this correction by removing genes with low signal, with little attention paid to their impact on downstream analyses.
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Bioinformatics, Oxford University Press (OUP), 2013, 29, pp.2146-2152. 〈10.1093/bioinformatics/btt350〉
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Contributeur : Gilles Celeux <>
Soumis le : vendredi 10 janvier 2014 - 16:20:27
Dernière modification le : jeudi 11 janvier 2018 - 06:22:14

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Andrea Rau, Mélina Gallopin, Gilles Celeux, Florence Jaffrezic. Data-based filtering for replicated high-throughput transcriptome sequencing experiments. Bioinformatics, Oxford University Press (OUP), 2013, 29, pp.2146-2152. 〈10.1093/bioinformatics/btt350〉. 〈hal-00927025〉

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