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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 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.

SUBMITTER: Gauthier M

PROVIDER: S-EPMC7676475 | biostudies-literature | 2020 Dec

REPOSITORIES: biostudies-literature

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

Gauthier Marine M Agniel Denis D Thiébaut Rodolphe R Hejblum Boris P BP

NAR genomics and bioinformatics 20201119 4

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 demo ...[more]

PMID: 33575637

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