| |
Normalisation method
|
|---|
| |
Scale
|
BABAR
|
Quantile
|
print-tip loess
|
|
No. of genes
|
Power
|
Error
|
Power
|
Error
|
Power
|
Error
|
Power
|
Error
|
|
118
|
0.66
|
0.022
|
0.62
|
0.030
|
0.51
|
0.032
|
0.48
|
0.033
|
|
220
|
0.73
|
0.034
|
0.58
|
0.053
|
0.57
|
0.051
|
0.51
|
0.056
|
|
407
|
0.77
|
0.055
|
0.62
|
0.088
|
0.61
|
0.089
|
0.56
|
0.091
|
|
756
|
0.82
|
0.088
|
0.67
|
0.150
|
0.65
|
0.158
|
0.63
|
0.163
|
|
1402
|
0.87
|
0.144
|
0.76
|
0.260
|
0.74
|
0.300
|
0.71
|
0.314
|
- The power and proportion of type I errors for a dataset consisting of 6 simulated microarrays [23] were calculated via the samr R package [25]. Analysis followed one of four methods of microarray data normalisation for different numbers of differentially expressed genes. For BABAR, analyses were run after background correction (subtraction) with 'within arrays' block-by-block centering of the medians, 'between arrays' cyclic loess with averaging of the log2-ratios, and final median centering of the data. For the other methods, analyses were run after the data were background corrected (normexp) with a 'within arrays' method for data centering (printtiploess), followed by the appropriate 'between arrays' method (none, quantile or scale normalisation).
