dearseq: a variance component score test for RNA-seq differential analysis that effectively controls the false discovery rate
THIEBAUT, Rodolphe
Statistics In System biology and Translational Medicine [SISTM]
Bordeaux population health [BPH]
Leer más >
Statistics In System biology and Translational Medicine [SISTM]
Bordeaux population health [BPH]
THIEBAUT, Rodolphe
Statistics In System biology and Translational Medicine [SISTM]
Bordeaux population health [BPH]
Statistics In System biology and Translational Medicine [SISTM]
Bordeaux population health [BPH]
HEJBLUM, Boris
Statistics In System biology and Translational Medicine [SISTM]
Bordeaux population health [BPH]
< Leer menos
Statistics In System biology and Translational Medicine [SISTM]
Bordeaux population health [BPH]
Idioma
EN
Article de revue
Este ítem está publicado en
NAR Genomics and Bioinformatics. 2020-11-19, vol. 2, n° 4, p. lqaa093
Resumen en inglés
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. ...Leer más >
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.< Leer menos
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