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Table 1 Empirical type-I error rate and power for bipolar methylation detection

From: Nonparametric Bayesian clustering to detect bipolar methylated genomic loci

τ =0 Type-I error Power
  m =10 m =20 m =100 m =10 m =20 m =100
w=10% .079 .075 .087 .279 .580 .983
w=20% .090 .077 .082 .590 .875 .997
w=30% .082 .080 .094 .875 .976 .998
w=40% .084 .083 .088 .887 .986 .997
w=50% .085 .088 .088 .931 .995 .998
τ =0 . 32 Type-I error Power
  m =10 m =20 m =100 m =10 m =20 m =100
w=10% .032 .015 .006 .275 .556 .871
w=20% .034 .015 .004 .528 .771 .984
w=30% .031 .017 .008 .770 .937 .996
w=40% .027 .020 .008 .778 .946 .996
w=50% .025 .016 .006 .782 .951 .997
  1. The empirical type-I error rate and power are calculated from 5,000 simulations under significance level 0.05, for different number of reads m and for different cell-type proportion w. In all simulations, we set the threshold parameter δ= 0.35. The type-I error rates for the same m but different w are not the same because we set different methylation probability vectors for different w when generating data under H 0, although these probabilities are all sampled from beta(8, 8).