Comparative analysis of differential gene expression analysis tools for single-cell RNA sequencing data

BMC Bioinformatics

Table 2 Numbers of the detected DE genes, sensitivities, false positive rates, precisions, and accuracies of the nine tools using simulated data for an adjusted p-value or FDR of 0.05

	Number of detected DE genes	Sensitivity (\( \frac{\mathrm{TP}}{\mathrm{TP}+\mathrm{FN}} \))	False positive rate (\( \frac{\mathrm{FP}}{\mathrm{FP}+\mathrm{TN}} \))	Precision (\( \frac{\mathrm{TP}}{\mathrm{TP}+\mathrm{FP}} \))	Accuracy (\( \frac{\mathrm{TP}+ TN}{\mathrm{P}+\mathrm{N}} \))	F1 score (\( \frac{2 TP}{2 TP+ FP+ FN} \))
Monocle2	4664.6	0.785	0.172	0.337	0.824	0.472
EMDomics	2465.8	0.666	0.063	0.540	0.910	0.596
DESeq2	2182.6	0.739	0.039	0.677	0.939	0.707
D3E	1683.4	0.565	0.031	0.671	0.929	0.613
scDD	1155.8	0.505	0.008	0.875	0.943	0.640
MAST	954.4	0.470	0.001	0.986	0.946	0.637
edgeR	1161.2	0.557	0.003	0.959	0.953	0.705
SCDE	842	0.419	0.0003	0.994	0.942	0.590
SINCERA	633.6	0.312	0.001	0.984	0.931	0.474
DEsingle	1448.8	0.697	0.003	0.962	0.967	0.808
SigEMD	1456	0.682	0.005	0.937	0.964	0.790

ISSN: 1471-2105