Table 3 Simulation study 1, scenario 1 with nX=30, nY=10 and 100 runs
From: Identification of differentially expressed genes by means of outlier detection
Δ=1.5 | ||||
Nb. of | α | |||
DE genes | 0.1 | 0.05 | 0.01 | |
50 | \(\hat {p}_{m}\) | 0.85 | 1 | 1 |
% | 97.2 (2.8) | 91.1 (2.1) | 69.6 (6.7) | |
100 | \(\hat {p}_{m}\) | 1 | 1 | 1 |
% | 93.7 (2.5) | 86.0 (3.5) | 60.1 (5.0) | |
200 | \(\hat {p}_{m}\) | 1 | 1 | 1 |
% | 84.9 (2.6) | 70.5 (3.5) | 34.9 (3.4) | |
Δ=2 | ||||
Nb. of | α | |||
DE genes | 0.1 | 0.05 | 0.01 | |
50 | \(\hat {p}_{m}\) | 0.13 | 0.33 | 0.98 |
% | 99.7 (0.7) | 99.2 (1.2) | 93.2 (3.6) | |
100 | \(\hat {p}_{m}\) | 0.38 | 0.88 | 1 |
% | 99.6 (0.6) | 98.4 (1.0) | 88.4 (3.2) | |
200 | \(\hat {p}_{m}\) | 0.99 | 1 | 1 |
% | 97.8 (1.1) | 92.1 (1.9) | 65.3 (4.0) | |
Δ=3 | ||||
Nb. of | α | |||
DE genes | 0.1 | 0.05 | 0.01 | |
50 | \(\hat {p}_{m}\) | 0.00 | 0.00 | 0.02 |
% | 100 | 100 | 96.0 (0.3) | |
100 | \(\hat {p}_{m}\) | 0.00 | 0.00 | 0.14 |
% | 100 | 100 | 99.8 (0.5) | |
200 | \(\hat {p}_{m}\) | 0.01 | 0.11 | 0.97 |
% | 100.0 (0.1) | 99.9 (0.2) | 98.1 (1.1) |