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Table 2 Comparison of effect on population variance between different normalization strategies

From: Normalization of array-CGH data: influence of copy number imbalances

 

P-values for data sets

 

Data set

1 [23]

2 [25]

3 [20]

4 [8]

5

6

8 [21]

 

Nbr of samples

7

28

10

52

8

8

8

 

Platform

BAC 32 K

BAC 32 K

BAC 32 K

BAC 1 Mb

Agilent 244 K

Agilent 44 K

BAC 32 K

popLowess vs Lowess

All populations

1.1e-4

7.0e-12

5.6e-8

3.4e-28

2.5e-05

1.6e-4

7.2e-5

 

Population 1

7.8e-3

1.4e-5

9.8e-4

9.9e-32

2.0e-3

2.0e-3

3.9e-3

 

Population 2

7.8e-3

1.5e-6

9.8e-4

7.4e-4

2.0e-3

0.09

3.5e-2

 

Population 3

0.23

6.3e-3

2.0e-2

2.5e-7

0.25

0.09

0.25

popLowess vs Median

All populations

< 1e-32

< 1e-32

< 1e-32

< 1e-32

< 1e-32

< 1e-32

< 1e-32

 

Population 1

7.8e-3

3.7e-9

9.8e-4

9.9e-32

2.0e-3

2.0e-3

3.9e-3

 

Population 2

6.3e-2

1.4e-5

0.17

3.8e-2

0.50

0.09

0.14

 

Population 3

0.23

9.0e-5

0.25

0.28

0.25

0.50

0.75

  1. P-values for different populations for data sets are shown. The test corresponds to the null hypothesis that lower standard deviations for popLowess are obtained by chance. Population 1 always relates to the largest identified population (# probes), population 2 to the second largest and population 3 to the smallest.
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BMC Genomics

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