Dearseq: a variance component score test for RNA-seq differential analysis that effectively controls the false discovery rate. Issue 4 (19th November 2020)

• Record Type: Journal Article
• Authors: Gauthier, Marine; Agniel, Denis; Thiébaut, Rodolphe; Hejblum, Boris P
• Is Part Of: NAR genomics and bioinformatics. Volume 2:Issue 4(2020)
• Publication Date: 2020-11-19
• Subjects: Genomics -- Periodicals; Bioinformatics -- Periodicals; 572.8
• DOI: 10.1093/nargab/lqaa093
• Languages: English
• ISSNs: 2631-9268

Abstract

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.