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dearseq: a variance component score test for RNA-Seq differential analysis that effectively controls the false discovery rate

Abstract : RNA-seq studies are growing in size and popularity. We provide evidence that the most commonly used methods for differential expression analysis (DEA) may yield too many false positive results in some situations. We present dearseq, a new method for DEA which controls the FDR without making any assumption about the true distribution of RNA-seq data. We show that dearseq controls the FDR while maintaining strong statistical power compared to the most popular methods. We demonstrate this behavior with mathematical proofs, simulations, and a real data set from a study of Tuberculosis, where our method produces fewer apparent false positives.
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https://hal.inria.fr/hal-02138664
Contributor : Boris Hejblum Connect in order to contact the contributor
Submitted on : Friday, May 24, 2019 - 8:05:52 AM
Last modification on : Tuesday, October 19, 2021 - 4:09:43 PM

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Marine Gauthier, Denis Agniel, Rodolphe Thiébaut, Boris Hejblum. dearseq: a variance component score test for RNA-Seq differential analysis that effectively controls the false discovery rate. NAR Genomics and Bioinformatics, Oxford University Press, 2020, 2 (4), pp.lqaa093. ⟨10.1093/nargab/lqaa093⟩. ⟨hal-02138664⟩

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