Table 3

Summary of comparing Misty Mountain with state of the art flow cytometry specific clustering methods

Data set

Manually gated 2D barcoding&

Simulated 5D Gaussians

Simulated 2D non-convex

3D rituximab

4D GvHD

Manually gated 4D

OP9


Misty Mountain

accuracy

sens

(%)

100

100

100

-

-

100


spec

(%)

100

100

100

-

-

100


CPU

(sec)

10

196

6

0.3

0.8

3.6


FLAME

accuracy

sens

(%)

20a

60b

-

0d*

100d

-

-

-


spec

(%)

33a

50b

-

0d*

100d

-

-

-


CPU

(sec)

5.104

>3.105

1.104

10

360

1.4 · 104


flowClust

accuracy

sens

(%)

45a*

60b*

100c

0c*

100d

-

-

60d*

60*


spec

(%)

60a*

55b*

100c

0c*

100d

-

-

75d*

38*


CPU

(sec)

5.104

4.104

7200

43

480

3660


flowMerge

accuracy

sens

(%)

25

100

0

-

-

80


spec

(%)

45

100

0

-

-

57


CPU

(sec)

1.3 · 105

1.27 · 105

7200

124

1020

8400


flowJo

accuracy

sens

(%)

45

-

-

-

-

-


spec

(%)

47

-

-

-

-

-


CPU

(sec)

1-10

-

-

1-10

1-10

-


a optimal cluster number: 12

b optimal cluster number: 24

a*optimal cluster number: 15

b*optimal cluster number: 22

c optimal cluster number: 5

c* optimal cluster number: 2

d optimal cluster number: 1

d* optimal cluster number: 4

* optimal cluster number: 8

&to save CPU time a data set, reduced by 80%, has been analyzed by FLAME, flowClust and flowJo

sens (sensitivity) = (# of correctly assigned clusters)/(# of clusters in gold standard)

spec(specificity) = (# of correctly assigned clusters)/(total # of assigned clusters)

Gold standards were independent expert manual clustering for experimental data and specified clusters for simulated data.

Sugár and Sealfon BMC Bioinformatics 2010 11:502   doi:10.1186/1471-2105-11-502

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