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Table 5 The comparison of different methods for the average evaluation values in all cells

From: LightCpG: a multi-view CpG sites detection on single-cell whole genome sequence data

Dataset

Methods

Acc(%)

AUC

Fscore(%)

MCC(%)

SP

SE(%)

HCCs

LightCpG

92.06

0.9616

84.66

78.97

93.73

86.84

 

DeepCpG

92.34

0.9689

86.42

81.24

92.95

90.59

 

RF Zhang

88.41

0.9351

79.93

72.08

89.38

85.59

HepG2

LightCpG

83.20

0.9213

81.73

67.36

89.96

78.32

 

DeepCpG

84.17

0.9248

82.52

68.22

85.27

83.40

 

RF Zhang

81.16

0.8942

80.17

63.20

87.39

76.29

  1. 1LightCpG employs three types of features (sequence feature, structural feature and positional feature) and LightGBM [36] to identify the CpG sites
  2. 2DeepCpG [35] embodies the connection between various cells by using the deep learning model Gated Recurrent Network (GRU) and also extracts features from the DNA sequence by the convolutional neural network (CNN) and one additional fully connected hidden layer. Then DeepCpG uses Fully Connected Neural Network to identify CpG sites
  3. 3RF Zhang [1] extracts the genomic positional features, neighbor features, sequence properties and sic-regulatory elements to identify the CpG sites
  4. 4The boldface is the best value in the column