Table 3

Assessment of algorithm performance on data simulated according to a model with homoscedastic multiplicative error plus additive (background) error



Power
 Rate of false positives
 RMS bias (×10-2)



(%)
 f
 q
 Naive
 NoSe-CoLoR
 Naive
 NoSe-CoLoR
 5th percentile
 95th percentile
10
 1.5
 0
 0.266
 0.380
 1.607
 1.089
 1.840
 6.824
10
 1.5
 1
 0.127
 0.317
 7.791
 0.888
 7.227
 34.413
10
 2.5
 0
 0.628
 0.636
 1.687
 1.117
 1.859
 8.019
10
 2.5
 1
 0.292
 0.630
 9.987
 0.970
 9.842
 37.617
20
 1.5
 0
 0.275
 0.384
 1.468
 1.031
 2.006
 6.927
20
 1.5
 1
 0.126
 0.296
 8.857
 0.895
 9.741
 34.407
20
 2.5
 0
 0.635
 0.646
 1.361
 1.384
 2.228
 7.120
20
 2.5
 1
 0.282
 0.608
 8.887
 1.063
 10.778
 34.203

The proportion, , among all genes of those for which the expression level has been changed is either 10% or 20%. The ratio, f, of treated expression level to mean control expression level is varied between 1.5 and 2.5. The bias multiplier q is either zero (no bias) or 1 (bias as measured in the analysis of the real data). The power is the mean number of correct discriminations achieved in the test divided by the number of true changes (59 and 119 for = 10% and = 20%, respectively). The false-positive score is the mean number of incorrect discriminations divided by the expected number at the nominal type-I error rate of 0.01. The expected number of false positives is 5.4 when = 10% and 4.8 when = 20%. The RMS bias is computed from the bias as estimated as described in the text. Reported here are the 5th and 95th percentiles over the simulated datasets.

Kepler et al. Genome Biology 2002 3:research0037.1   doi:10.1186/gb-2002-3-7-research0037