Table 3

Performance of Hwang et al.'s method on simulated data for scenario I

DE
 nonDE
 FP (%)
 TP (%)
 FN (%)
 TN (%)
 Global error
 Global error R(q2)
Independent case: n = 3000, common = 0, DE1 = 1000, DE2 = 800
 320
 2,680
 320 (10.7)
 0
 0
 2,680 (89.3)
 320
 0









A: n = 3000, common = 700, DE1 = 1000, DE2 = 800







     Case A1
 1,121
 1,879
 440 (19.1)
 681 (97.3)
 19 (2.7)
 1,860 (80.9)
 459
 82
     Case A2
 409
 2,591
 188 (8.2)
 221 (31.6)
 479 (68.4)
 2,112 (91.8)
 667
 544









B: n = 3000, common = 200, DE1 = 700, DE2 = 500







     Case B1
 999
 2,001
 805 (28.8)
 194 (97.0)
 6 (3.0)
 1,996 (71.2)
 811
 31*
     Case B2
 427
 2,573
 333 (11.9)
 94 (47.0)
 106 (53.0)
 2,467 (88.1)
 439
 165









C: n = 3000, common = 100, DE1 = 500, DE2 = 400







     Case C1
 816
 2,185
 718 (24.8)
 97 (97.1)
 3 (2.9)
 2,182 (75.2)
 721
 19*
     Case C2
 346
 2,654
 299 (10.3)
 47 (47.0)
 53 (53.0)
 2,601 (89.7)
 352
 84

Average simulation results: we present the results from Hwang et al.'s method on the simulated data under scenario I. DE1 and DE2 are the differentially expressed genes in the first and the second experiment respectively. We used the Fisher's weighted F defined as Math, where wk is the weight for the kth experiment and pgk is the p value for the gene g in the experiment k. We present the non-parametric rule to select the differentially expressed (DE) genes, as suggested by the authors. The method is implemented in Matlab. In the last column we report the Global error (FP + FN) of our procedure for q2 (see Table 2) for ease of comparison. *There is no ratio larger than 2 so the maximum rule has been used in this case.

Blangiardo and Richardson Genome Biology 2007 8:R54   doi:10.1186/gb-2007-8-4-r54