GAUTHIER, Marine; AGNIEL, Denis; THIEBAUT, Rodolphe; HEJBLUM, Boris

doi:10.1093/nargab/lqaa093

Statistics In System biology and Translational Medicine [SISTM]
Bordeaux population health [BPH]

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NAR Genomics and Bioinformatics. 2020-11-19, vol. 2, n° 4, p. lqaa093

Résumé en anglais

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 that controls the false discovery rate (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.< Réduire

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https://oskar-bordeaux.fr/handle/20.500.12278/26840

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http://dx.doi.org/10.1093/nargab/lqaa093

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Bordeaux Population Health Research Center (BPH) - UMR 1219

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

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