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Table 1 Area Under the Curve (AUC) of specificity and sensitivity of SAM test after the normalisations, for Albers' model with increasing percentage of background level with and without replacing negative values.

From: A comparison on effects of normalisations in the detection of differentially expressed genes

  10% bg 10% bg replaced 50% bg 50% bg replaced 150% bg 150% bg replaced
normalization AUC rank AUC rank AUC rank AUC rank AUC rank AUC rank
Raw 0.9 1 0.9 1 0.79 1 0.82 1 0.55 1 0.68 2
Global 0.91 2 0.91 2 0.84 2 0.86 2 0.66 2 0.74 4
GLOG 0.93 3 0.93 3 0.9 8 0.89 3 0.78 10 0.58 1
Lowess 0.94 6 0.93 3 0.86 3 0.9 6 0.69 6 0.73 3
P-Lowess 0.94 6 0.93 3 0.87 4 0.91 9 0.68 5 0.78 10
NeuralNet 0.93 3 0.93 3 0.87 4 0.91 9 0.69 6 0.76 5
OLIN 0.94 6 0.93 3 0.87 4 0.89 3 0.67 3 0.76 5
OSLIN 0.94 6 0.93 3 0.87 4 0.89 3 0.67 3 0.77 7
qsplineR N0.94 6 0.93 3 0.92 9 0.9 6 0.72 8 0.77 7
qsplineG 0.93 3 0.93 3 0.92 9 0.9 6 0.72 8 0.77 7
  1. For each simulated scenario is also reported the ranking of the normalisations according to the AUC: the bigger the rank, the better the normalisation.