A full Bayesian partition model for identifying hypo- and hyper-methylated loci from single nucleotide resolution sequencing data

Table 2 Similar to Table 1, but this comparison is based on the second set of simulation studies (with subject effect)

		Logistic	DSS	z-test	Bayesian
		regression
γ=0.01	FDR	0.0027	0	0.0034	0.0069
	mdFDR	0.0029	0	0.0036	0.008
	TPR	0.1953	0.0171	0.183	0.2494
	TPR_hypo	0.1948	0.0167	0.1918	0.2545
	TPR_hyper	0.1958	0.0176	0.171	0.2418
γ=0.05	FDR	0.0097	0	0.0113	0.014
	mdFDR	0.0097	0	0.0113	0.0141
	TPR	0.3554	0.0364	0.3235	0.4038
	TPR_hypo	0.3604	0.0375	0.3392	0.419
	TPR_hyper	0.3494	0.035	0.3044	0.3853
γ=0.1	FDR	0.0119	0	0.0142	0.0156
	mdFDR	0.0119	0	0.0142	0.0156
	TPR	0.5053	0.0668	0.4588	0.5745
	TPR_hypo	0.5021	0.0692	0.4637	0.5836
	TPR_hyper	0.509	0.0641	0.453	0.5637
γ=0.15	FDR	0.012	0	0.0147	0.0156
	mdFDR	0.012	0	0.0147	0.0156
	TPR	0.6385	0.1074	0.5939	0.7158
	TPR_hypo	0.6309	0.112	0.5905	0.7205
	TPR_hyper	0.6477	0.1016	0.5981	0.7101
γ=0.2	FDR	0.0116	0.0001	0.0146	0.0153
	mdFDR	0.0116	0.0001	0.0146	0.0153
	TPR	0.7546	0.1576	0.7203	0.8254
	TPR_hypo	0.7459	0.1612	0.7129	0.8287
	TPR_hyper	0.7659	0.1529	0.7298	0.821

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