Table 2. Performance Investigation for synthetic dataset.

Performance Evaluation	edgeR	SAMSeq	Voom.limma	t-test	Proposed
5% outliers
Sensitivity	0.36	0.015	0.493	0.046	0.546
Specificity	0.761	0.984	0.325	0.046	0.314
MER	0.774	0.89	0.691	0.867	0.537
FDR	0.269	0.821	0.256	0.216	0.085
AUC	0.664	0.515	0.694	0.32	0.744
pAUC	0.057	0.009	0.059	0.006	0.024
ACC	0.226	0.11	0.309	0.133	0.463
PPV	0.731	0.179	0.744	0.784	0.915
NPV	0.761	0.984	0.325	0.046	0.314
10% outliers
Sensitivity	0.372	0.019	0.391	0.046	0.476
Specificity	0.693	0.982	0.346	0.046	0.27
MER	0.812	0.884	0.796	0.859	0.685
FDR	0.28	0.314	0.276	0.176	0.066
AUC	0.581	0.515	0.57	0.342	0.71
pAUC	0.048	0.01	0.036	0.006	0.024
ACC	0.188	0.116	0.204	0.141	0.315
PPV	0.72	0.686	0.724	0.824	0.934
NPV	0.693	0.982	0.346	0.046	0.27
15% outliers
Sensitivity	0.364	0.018	0.421	0.048	0.64
Specificity	0.764	0.985	0.291	0.048	0.342
MER	0.445	0.889	0.47	0.869	0.227
FDR	0.014	0.732	0.0323	0.213	0.075
AUC	0.634	0.516	0.642	0.337	0.748
pAUC	0.055	0.01	0.047	0.007	0.029
ACC	0.555	0.111	0.53	0.131	0.773
PPV	0.986	0.268	0.9677	0.787	0.925
NPV	0.764	0.985	0.291	0.048	0.342
20% outliers
Sensitivity	0.405	0.017	0.439	0.046	0.612
Specificity	0.812	0.979	0.247	0.046	0.352
MER	0.702	0.885	0.652	0.878	0.216
FDR	0.058	0.217	0.055	0.366	0.069
AUC	0.742	0.51	0.724	0.333	0.745
pAUC	0.064	0.01	0.053	0.006	0.022
ACC	0.298	0.115	0.348	0.122	0.784
PPV	0.942	0.783	0.945	0.634	0.931
NPV	0.812	0.979	0.247	0.046	0.352