Wilcoxon rank-sum test still outperforms dearseq after accounting for the normalization impact in semi-synthetic RNA-seq data simulation
Year: 2022
Type: preprint
Abstract: In this response to the correspondence by Hejblum et al. [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]. 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]. 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.
Language: English
Subfield: Molecular Biology
Domain: Life Sciences
Open Access status: green
Awards: Collaborative Research: Development of Classification Theory and Methods for Objective Asymmetry, Sample Size Limitation, Labeling Ambiguity, and Feature Importance, CAREER: Advancing the Bioinformatic Infrastructure and Methodology for Single-cell RNA Sequencing, Transfer: DNA Methylation Canyons in Human Cancers: Methods, Target Genes and Functional Consequences, Computational and Experimental Modeling of Alternative Polyadenylation, Statistical methods for elucidating regulatory mechanisms and functional impacts of transcriptome variation at population and single-cell scales, Robust Identification and accurate quantification of RNA transcripts on a system wide scale
OpenAlex ID: W4281618576