| 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).