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Table 2 Number of classes obtained for various clustering methods applied to simulated data

From: Model-based clustering of DNA methylation array data: a recursive-partitioning algorithm for high-dimensional data arising as a mixture of beta distributions

 

Case 1 (5 true classes)

 

Case 2 (4 true classes)

 

Method

J

Median

Mean

SD

J

Median

Mean

SD

DynTree

25

3

2.5

0.50

25

2

2.0

0.00

 

50

3

2.5

0.50

50

2

2.0

0.00

 

500

3

2.7

0.58

500

2

2.0

0.00

 

1000

3

2.8

0.59

1000

2

2.0

0.00

HOPACH (best)

25

40

38.0

12.10

5

17

18.9

9.10

 

50

35

35.4

11.38

10

14

15.0

8.27

 

500

23

23.0

9.52

25

25

24.7

9.80

 

1000

23

23.1

9.47

50

25

25.3

7.34

HOPACH (greedy)

25

8

13.4

14.41

5

5

7.1

6.35

 

50

6

11.9

12.66

10

5

7.1

7.11

 

500

5

6.6

5.19

25

7.5

10.8

8.52

 

1000

4

6.2

4.41

50

8

10.1

7.85

RPMM

25

8

7.7

2.00

5

2

2.0

0.10

 

50

5

5.6

1.32

10

2

2.4

2.28

 

500

5

5.0

0.22

25

4

4.0

0.20

 

1000

5

5.0

0.00

50

4

4.1

0.58

  1. DynTree = Hierarchical clustering with classes determined by dynamic tree cutting
  2. HOPACH(best) = HOPACH with 'best' number of classes
  3. HOPACH(greedy) = HOPACH with 'greedy' number of classes
  4. RPMM = Recursively partitioned mixture model employing BIC
  5. J = Number of loci considered in analysis
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BMC Bioinformatics

ISSN: 1471-2105

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