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Table 1 Simulation results compared the number of true positives among different methods

From: High-dimensional omics data analysis using a variable screening protocol with prior knowledge integration (SKI)

Positivea 1% 5% 10%
%b σ 2 x c σ 2 z d α e SISf SKIg Ph SIS SKI P SIS SKI P
0.0 1 1 0.075 38.96 38.94 36.36 45.78 45.72 43.63 47.66 47.63 45.63
0.5 1 1 0.275 38.53 43.06 45.22 45.66 47.65 48.54 47.53 48.85 49.13
1.0 1 1 0.384 38.5 46.34 47.99 45.65 48.9 49.58 47.49 49.51 49.83
0.0 1 3 0.090 39.10 38.97 35.01 45.81 45.80 42.94 47.71 47.72 44.03
0.5 1 3 0.249 38.92 42.55 43.85 45.80 47.31 48.28 47.57 48.55 49.10
1.0 1 3 0.368 39.04 45.81 47.58 45.88 48.60 49.44 47.65 49.21 49.73
0.0 3 1 0.113 36.84 36.43 35.77 44.61 44.01 43.37 46.69 46.57 46.19
0.5 3 1 0.261 37.27 42.16 44.90 45.15 47.36 48.34 47.07 48.56 49.03
1.0 3 1 0.374 36.91 46.01 48.89 44.76 49.42 49.51 47.12 49.86 49.90
0.0 3 3 0.104 37.84 37.48 35.19 45.73 45.43 44.07 47.63 47.53 45.93
0.5 3 3 0.264 37.26 42.52 44.48 45.03 47.35 48.26 47.19 48.58 49.00
1.0 3 3 0.355 37.05 45.20 47.37 45.1 48.6 49.39 47.05 49.36 49.76
  1. aTop 1, 5 and 10% variables were selected respectively under different settings
  2. bthe percentage of non-zero β’s overlapped with each other in two datasets
  3. c σ 2 x : the variance added in internal dataset to generate response Y x
  4. d σ 2 z : the variance added in external dataset to generate response Y z
  5. e α: the estimated value of α which control the weight of two ranks in geometric mean
  6. fSIS: variables were sorted by marginal correlation using only internal dataset
  7. gSKI: variables were sorted by weighted geometric mean of two marginal correlation based ranks using two dataset
  8. hPool: two dataset were pooled together and treated as a single dataset, and then variables were sorted by marginal correlation