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Table 3 The sensitivity performance about method comparison on approach-specific datasets (K = 3)

From: Integrative enrichment analysis: a new computational method to detect dysregulated pathways in heterogeneous samples

ID GSA-specific PADOG-specific IEA-specific MRGSE-specific ORA-specific GLOBALTEST-specific GSVA-specific PLAGE-specific
GSA (0.10,0.12) (0.22,0.28) (0.35,0.29) (0.042,0.014) (0.48,0.40) (0.25,0.27) (0.19,0.22) (0.21,0.22)
PADOG (0.11,0.15) (0.051,0.11) (0.24,0.20) (0.057,0.011) (0.41,0.28) (0.14,0.17) (0.13,0.12) (0.12,0.15)
IEA (0.11,0.17) (0.13,0.13) (0.044,0.057) (0.12,0.14) (0.012,0.0043) (0.092,0.11) (0.12,0.13) (0.10,0.12)
MRGSE (0.73,0.19) (0.46,0.31) (0.59,0.27) (0.020,0.012) (0.56,0.077) (0.47,0.32) (0.46,0.31) (0.50,0.32)
ORA (0.24,0.23) (0.49,0.27) (0.24,0.21) (0.50,0.60) (0.13,0.085) (0.39,0.29) (0.37,0.28) (0.40,0.29)
GLOBALTEST (0.11,0.13) (0.013,0.028) (0.052,0.084) (0.00011,0.00015) (0.083,0.10) (0.011,0.044) (0.037,0.086) (0.025,0.073)
GSVA (0.11,0.11) (0.32,0.28) (0.33,0.26) (0.060,0.080) (0.21,0.23) (0.24,0.26) (0.013,0.017) (0.21,0.25)
PLAGE (0.097,0.11) (0.063,0.18) (0.095,0.15994) (0.010,0.014) (0.19,0.10) (0.036,0.11) (0.034,0.076) (0.022,0.066)
  1. The performance of an approach on its specific dataset is highlighted in bold. And the performance of comparable approaches on some specific dataset is highlighted in bolditalic