Wilcoxon rank-sum test still outperforms dearseq after accounting for the normalization impact in semi-synthetic RNA-seq data simulation

In this response to the correspondence by Hejblum et al. [[1][1]], we clarify the reasons why we ran the Wilcoxon rank-sum test on the semi-synthetic RNA-seq samples without normalization, and why we could only run dearseq with its built-in normalization, in our published study [[2][2]]. We also argue that no normalization should be performed on the semi-synthetic samples. Hence, for a fairer method comparison and using the updated dearseq package by Hejblum et al., we re-run the six differential expression methods (DESeq2, edgeR, limma-voom, dearseq, NOISeq, and the Wilcoxon rank-sum test) without normalizing the semi-synthetic samples, i.e., under the “No normalization” scheme in [[1][1]]. Our updated results show that the Wilcoxon rank-sum test is still the best method in terms of false discovery rate (FDR) control and power performance under all settings investigated. ### Competing Interest Statement The authors have declared no competing interest. [1]: #ref-1 [2]: #ref-2