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.
Researchers at INRA, France propose a data-driven method based on the Jaccard similarity index to calculate a filtering threshold for replicated RNA-seq data. In comparisons with alternative data filters regularly used in practice, they demonstrate the effectiveness of the proposed method to correctly filter lowly expressed genes, leading to increased detection power for moderately to highly expressed genes. Interestingly, this data-driven threshold varies among experiments, highlighting the interest of the method proposed here.
AVAILABILITY: The proposed filtering method is implemented in the R package HTSFilter available at – http://www.bioconductor.org/packages/release/bioc/html/HTSFilter.html
CONTACT: andrea.rau@jouy.inra.fr
Rau A, Gallopin M, Celeux G, Jaffrézic F. (2013) Data-based filtering for replicated high-throughput transcriptome sequencing experiments. Bioinformatics [Epub ahead of print]. [abstract]
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