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Table 3 Performance assessment for LNCaP analysis by copy number

From: BayMeth: improved DNA methylation quantification for affinity capture sequencing data using a flexible Bayesian approach

Copy number Number of bins Method Mean bias Mean of squared differences Spearman correlation
2 18,010 BayMeth 0.04 0.04 0.78
   BayMeth (SssI-free) 0.08 0.05 0.79
   BayMeth (CNV-unaware) -0.11 0.06 0.78
   BayMeth (SssI-free, CNV-unaware) -0.05 0.05 0.79
   Batman 0.03 0.06 0.74
   MEDIPS -0.23 0.11 0.76
   BALM -0.29 0.16 0.78
3 65,982 BayMeth 0.05 0.04 0.80
   BayMeth (SssI-free) 0.09 0.05 0.80
   BayMeth (CNV-unaware) -0.01 0.04 0.80
   BayMeth (SssI-free, CNV-unaware) 0.05 0.04 0.80
   Batman 0.11 0.06 0.77
   MEDIPS -0.19 0.09 0.76
   BALM -0.20 0.10 0.79
4 256,074 BayMeth 0.05 0.04 0.81
   BayMeth (SssI-free) 0.10 0.05 0.81
   BayMeth (CNV-unaware) 0.05 0.04 0.81
   BayMeth (SssI-free, CNV-unaware) 0.11 0.06 0.81
   Batman 0.16 0.08 0.79
   MEDIPS -0.17 0.09 0.76
   BALM -0.12 0.07 0.80
5 11,790 BayMeth 0.04 0.03 0.83
   BayMeth (SssI-free) 0.07 0.05 0.82
   BayMeth (CNV-unaware) 0.09 0.04 0.83
   BayMeth (SssI-free, CNV-unaware) 0.12 0.06 0.82
   Batman 0.18 0.08 0.80
   MEDIPS -0.12 0.07 0.80
   BALM -0.08 0.05 0.82
  1. Results are shown for 100-bp bins with a mappability of at least 75% stratified into the four most frequent copy number states. A threshold of 13 was applied for the depth of the SssI-control. Taking SssI information into account a uniform prior for the methylation level was used. In the SssI-free version a Dirac-Beta-Dirac mixture with weights fixed to 0.1, 0.8 and 0.1 was used. CNV, copy number variation; MSE, mean of squared differences.