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Table 1 Assessment of algorithm performance on data simulated according to the homoscedastic error model

From: Normalization and analysis of DNA microarray data by self-consistency and local regression

    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
  1. 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.