Neglecting the impact of normalization in semi-synthetic RNA-seq data simulations generates artificial false positives

• Published in: Genome Biology Vol. 25; no. 1; pp. 281 - 5
• Main Authors: Hejblum, Boris P, Ba, Kalidou, Thiébaut, Rodolphe, Agniel, Denis
• Format: Journal Article
• Language: English
• Published: England BioMed Central 30.10.2024; BMC
• Subjects: Computer Simulation; Correspondence; Differential expression analysis; False Positive Reactions; Gene Expression Profiling - methods; Human population samples; Humans; Related research article; RNA-Seq - methods; RNA-seq data simulation; Sequence Analysis, RNA - methods; Human population samples; RNA-seq data simulation; Differential expression analysis; Related research article
• ISSN: 1474-760X; 1474-7596; 1474-760X
• DOI: 10.1186/s13059-024-03231-9

A recent study reported exaggerated false positives by popular differential expression methods when analyzing large population samples. We reproduce the differential expression analysis simulation results and identify a caveat in the data generation process. Data not truly generated under the null hypothesis led to incorrect comparisons of benchmark methods. We provide corrected simulation results that demonstrate the good performance of dearseq and argue against the superiority of the Wilcoxon rank-sum test as suggested in the previous study.