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Table 1 Melissa robustly imputes CpG methylation states on sub-sampled ENCODE scRRBS and scWGBS synthetic data. Entries with italics denote the model with the highest performance in terms of AUC

From: Melissa: Bayesian clustering and imputation of single-cell methylomes

 

Pseudo scRRBS

Pseudo scWGBS

Model

AUC 20% cov

AUC 50% cov

AUC 20% cov

AUC 50% cov

Melissa

0.96 (7.3×10−4)

0.96 (6.8×10−4)

0.96 (6.3×10−4)

0.96 (6.6×10−4)

DeepCpG

0.94 (1.5×10−3)

0.94 (1.5×10−3)

0.96 (1.4×10−3)

0.96 (1.4×10−3)

BPRMeth

0.88 (2.2×10−3)

0.91 (2.5×10−3)

0.90 (1.9×10−3)

0.92 (1.5×10−3)

RF

0.79 (3.2×10−3)

0.87 (2.0×10−3)

0.83 (2.2×10−3)

0.89 (2.1×10−3)

Melissa rate

0.88 (1.8×10−3)

0.88 (1.3×10−3)

0.70 (2.2×10−3)

0.71 (2.5×10−3)

Rate

0.82 (2.6×10−3)

0.84 (2.5×10−3)

0.76 (4.2×10−3)

0.77 (3.0×10−3)

  1. Imputation performance in terms of AUC as we vary the proportion of covered CpGs used for training. Higher values correspond to better imputation performance. For each CpG coverage setting, a total of 10 random splits of the data to training and test sets was performed; shown are the mean AUC value together with two standard deviations of the estimate in parenthesis. Note that DeepCpG was trained once on two chromosomes; hence, the values do not change as we vary the CpG coverage