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Table 1 Area under the ROC curve in synthetic data

From: Probe-level estimation improves the detection of differential splicing in Affymetrix exon array studies

Differentially spliced exons Noise level σ PECA-SI RMA-LM PLIER-LM RMA-SI PLIER-SI RMA-MIDAS PLIER-MIDAS FIRMA
1 0.7 0.99 0.99 0.98 0.99 0.94 0.99 0.95 0.99
2 0.7 0.99 0.98 0.96 0.98 0.94 0.98 0.94 0.98
3 0.7 0.97 0.94 0.91 0.93 0.93 0.94 0.93 0.93
4 0.7 0.94 0.87 0.83 0.86 0.90 0.90 0.90 0.86
5 0.7 0.92 0.83 0.79 0.82 0.88 0.87 0.88 0.81
1 1.0 0.94 0.91 0.89 0.97 0.92 0.90 0.85 0.94
2 1.0 0.94 0.91 0.88 0.93 0.89 0.91 0.86 0.91
3 1.0 0.90 0.86 0.82 0.86 0.82 0.87 0.81 0.84
4 1.0 0.88 0.83 0.80 0.84 0.80 0.85 0.79 0.82
5 1.0 0.74 0.68 0.68 0.69 0.69 0.72 0.67 0.66
  1. The synthetic data were generated according to Equation 8 at two different noise levels, σ = 0.7 or σ = 1. The first column indicates the number of synthetic differential splicing events generated within a gene. At each combination of the noise level and the number of differentially spliced exons, 1,000 genes were investigated. In each case, the probe-level PECA-SI procedure (see Equation 6) was compared to the standard SI procedures RMA-SI, PLIER-SI, RMA-MIDAS and PLIER-MIDAS, the two-way ANOVA-based approaches RMA-LM and PLIER-LM, and the FIRMA algorithm. The largest area under the curve (AUC) value across the methods is indicated in bold.