Reply to \"A discriminative learning approach to differential expression analysis for single-cell RNA-seq\"

Multivariate logistic regression (mLR) has been recently proposed by Ntranos et al. to perform gene differential expression analyses of single-cell RNA-sequencing (scRNAseq) data. Herein we reproduce and extend some of their findings. We notably show that while mLR performs better in simulated datasets, these simulations do not recapitulate important features of experimental datasets. Indeed, our results suggest that MAST followed by Sidak aggregation of the p-values perform better than mLR on experimental datasets. Overall, we highlight that most of the new results obtained by Ntranos et al is likely due to the quantification of scRNAseq data at the transcript or transcript compatibility classes level, rather than the use of mLR.