Table 1

Assessment of algorithm performance on data simulated according to the homoscedastic error model



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



(%)
 f
 q
 Naive
 NoSeCoLoR
 Naive
 NoSeCoLoR
 5th percentile
 95th percentile
10
 1.5
 0
 0.318
 0.315
 1.024
 1.035
 0.937
 1.710
10
 1.5
 1
 0.127
 0.300
 0.929
 0.933
 16.559
 17.872
10
 2.5
 0
 0.989
 0.974
 1.004
 1.181
 1.524
 3.292
10
 2.5
 1
 0.689
 0.971
 0.955
 0.968
 15.776
 17.163
20
 1.5
 0
 0.327
 0.314
 0.975
 1.002
 1.079
 2.226
20
 1.5
 1
 0.129
 0.295
 0.883
 0.973
 16.380
 17.742
20
 2.5
 0
 0.985
 0.939
 1.000
 1.662
 3.359
 5.763
20
 2.5
 1
 0.684
 0.941
 0.889
 1.298
 15.279
 16.823

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