Table 2 Simulation study 1, scenario 1 with nX=nY=30 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 | \(\hat {p}_{m}\) | 0.08 | 0.20 | 0.80 |
% | 99.84 (0.54) | 99.56 (0.92) | 97.20 (2.2) | |
100 | \(\hat {p}_{m}\) | 0.13 | 0.45 | 0.99 |
% | 99.87 (0.34) | 99.34 (0.66) | 95.20 (2.3) | |
200 | \(\hat {p}_{m}\) | 0.80 | 0.99 | 1.00 |
% | 99.27 (0.56) | 96.70 (1.5) | 78.80 (4.0) | |
Δ=2 | ||||
Nb. of | α | |||
DE genes | 0.1 | 0.05 | 0.01 | |
50 | \(\hat {p}_{m}\) | 0.00 | 0.00 | 0.00 |
% | 100 | 100 | 100 | |
100 | \(\hat {p}_{m}\) | 0.00 | 0.00 | 0.05 |
% | 100 | 100 | 99.95 (0.22) | |
200 | \(\hat {p}_{m}\) | 0.01 | 0.03 | 0.95 |
% | 100 | 99.98 (0.09) | 98.50 (0.83) | |
Δ=3 | ||||
Nb. of | α | |||
DE genes | 0.1 | 0.05 | 0.01 | |
50 | \(\hat {p}_{m}\) | 0.00 | 0.00 | 0.00 |
% | 100 | 100 | 100 | |
100 | \(\hat {p}_{m}\) | 0.00 | 0.00 | 0.00 |
% | 100 | 100 | 100 | |
200 | \(\hat {p}_{m}\) | 0.00 | 0.00 | 0.00 |
% | 100 | 100 | 100 |