From: A gene sets approach for identifying prognostic gene signatures for outcome prediction
Datasets | 11823860_ST2 | Mitotic checkpoint | Cell_cycle_KEGG |
---|---|---|---|
Bild | 6.35 * # (1.23–32.2) p = 0.0256 | 2.88 (0.686–12.1) p = 0.148 | 1.13 (0.407–3.11) p = 0.819 |
Miller | 1.29 (0.297–5.63) p = 0.731 | 0.942 (0.269–3.3) p = 0.925 | 1.37 (0.547–3.41) p = 0.504 |
Oh | 4.72 (0.834–26.7) p = 0.0794 | 3.87 (0.792–18.9) p = 0.0944 | 2.07 (0.728–5.9) p = 0.172 |
Pawitan | 34.6 (4.94–242) p = 3.57e-4 | 11.9 (2.84–49.9) p = 7.1e-4 | 5.21 (1.97–13.8) p = 8.6e-4 |
Sorlie_1 | 6.84 (1.75–26.7) p = 0.00568 | 4.73 (1.46–15.3) p = 0.00953 | 2.07 (1.07–4.01) p = 0.0312 |
Sorlie_2 | 3.28 (0.29–46.9) p = 0.381 | 1.99 (0.308–12.8) p = 0.471 | 1.33 (0.319–5.57) p = 0.695 |
Sotiriou_1 | 27.3 (2.60–287) p = 0.0582 | 64.2 (2.22–1854) p = 0.0153 | 5.58 (1.19–26.20 p = 0.0296 |
Sotiriou_2 | 5.22 (1.63–16.8) p = 0.00549 | 3.13 (1.17–8.42) p = 0.0234 | 2.6 (1.24–5.44) p = 0.0113 |
Van de Vijver | 62.3 (17.7–219) p = 1.12e-10 | 8.8 (4.18–18.5) p = 1.05e-8 | 4.03 (2.37–6.85) p = 2.73e-7 |
Wang | 7.48 (2.78–20.1) p = 6.92e-5 | 2.73 (1.22–6.1) p = 0.0144 | 3.78 (1.89–7.55) p = 1.66e-4 |
Wiegelt | 2.00 (0.152–26.0) p = 0.597 | 1.40 (0.15–13.0) p = 0.769 | 1.25 (0.19–3.38) p = 0.764 |
West | 15.5 (0.73–329) p = 0.788 | 5.56 (0.635–12.1) p = 0.121 | 4.27 (1.20–15.1) p = 0.0246 |