Table 2 Performance of the three approaches for Study 2.

From: A model-free and distribution-free multi-omics integration approach for detecting novel lung adenocarcinoma genes

Method

\(\mathcal {S}\)

Selection size

\(\mathcal {P}_{\varvec{X}_1}\)

\(\mathcal {P}_{\varvec{X}_6}\)

\(\mathcal {P}_{\varvec{X}_{12}}\)

\(\mathcal {P}_{\varvec{X}_{22}}\)

\(\mathcal {P}_a\)

SIS

25%

6872.75

\(s_1\)

0.01

0.02

0.08

0

0

50%

8030.5

\(s_2\)

0.01

0.04

0.09

0

0

95%

9802.3

\(s_3\)

0.01

0.04

0.1

0.01

0

DC-SIS

25%

6584

\(s_1\)

0

0.02

0.16

0.01

0

50%

8111

\(s_2\)

0.02

0.03

0.21

0.01

0

95%

9761.15

\(s_3\)

0.02

0.05

0.22

0.01

0

MrDcGene

25%

327.75

\(s_1\)

0.53

0.26

0.92

0.1

0

50%

1071.5

\(s_2\)

0.57

0.49

0.97

0.15

0.02

95%

5313

\(s_3\)

0.62

0.56

0.98

0.21

0.04

  1. The minimum selection size \(\mathcal {S}\), the individual success rate \(\mathcal {P}_s\), and the overall success rate \(\mathcal {P}_a\) are demonstrated. The predetermined cutoffs for \(\mathcal {P}_s\) and \(\mathcal {P}_a\) are \(s_1 = [n/\log (n)]=37\), \(s_2 = 2s_1\) and \(s_3 = 3s_1\).