Table 1 comparison for unsupervised methods: Silhouette coefficients and number of genes for each cluster and unsupervised clustering method (no labels). Laplacian Eigenmaps+k-means leads to higher silhouette coefficients.

1

0.0200

65

0.7329

126

0.6535

103

2

0.3067

146

0.6221

60

0.7049

125

3

0.4078

180

0.7002

168

0.6862

174

4

0.4068

234

0.6840

198

0.6848

154

5

0.3401

255

0.7423

157

0.7831

97

6

0.2960

252

0.7033

130

0.7949

389

7

0.3442

90

0.6795

126

0.7369

120

8

0.6509

9

0.6800

65

0.6953

270

9

0.3900

254

0.6393

190

0.7800

91

10

0.2162

34

0.7130

187

0.7046

79

11

0.3056

112

0.6517

182

0.7606

141

12

0.3531

165

0.7162

155

0.7487

122

13

0.4636

182

0.6925

117

0.9889

3

14

0.4267

167

0.7422

205

0.7118

125

15

0.6529

114

0.6968

184

0.5997

85

16

0.1593

86

0.5266

9

0.7214

236

17

0.5488

13

0.6792

84

0.6839

83

18

0.4323

253

0.6956

211

0.7380

135

19

0.1749

20

0.7151

118

0.6466

72

20

0.3076

133

0.6926

170

0.7243

121

21

0.4314

174

0.7041

115

0.7461

199

22

0.4394

130

0.7342

116

0.7442

275

23

0.4538

210

0.7252

192

0.6849

115

24

0.4366

138

0.6792

151

0.8534

102