Neglecting normalization impact in semi-synthetic RNA-seq data simulation generates artificial false positives

Hejblum, B. P.; Ba, K.; Thiebaut, R.; Agniel, D.

doi:10.1101/2022.05.10.490529

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bioRxiv · 10.1101/2022.05.10.490529

Neglecting normalization impact in semi-synthetic RNA-seq data simulation generates artificial false positives

Hejblum, B. P.·Ba, K.·Thiebaut, R.·Agniel, D.

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Abstract

By reproducing differential expression analysis simulation results presented by Li et al, we identified 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 by Li et al. Please see related Research article with DOI 10.1186/s13059-022-02648-4.

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Hejblum, B. P., Ba, K., Thiebaut, R., Agniel, D.. 2022-05-11. Neglecting normalization impact in semi-synthetic RNA-seq data simulation generates artificial false positives. https://doi.org/10.1101/2022.05.10.490529

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