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Table 1 Summary of method performances from artificial data analysis

From: Robust extraction of functional signals from gene set analysis using a generalized threshold free scoring function

 

parameters (if any)

fixed size, up-regulation

fixed size, up and down-regulation

varying size, up-regulation

Varying size, up and down-regulation

included to fig. 1 and 2

 

w1

w2

Score A

Score B

Score A

Score B

Score A

Score B

Score A

Score B

 

GSZ-score

0

0.1

-0.0002

-0.1898

0.0867

0.184

0.0111

0.0837

0.1025

0.4976

 

GSZ-score

0.1

0.1

0.003

-0.0896

0.0883

0.26

0.0095

0.0988

0.1003

0.4669

 

GSZ-score

0.2

0.1

0.0048

0.0119

0.0891

0.3061

0.0065

-0.0085

0.0965

0.2528

 

GSZ-score

0.5

0.1

0.0074

0.1567

0.0898

0.3006

-0.0031

-0.355

0.0837

-0.0515

 

GSZ-score

0

0.3

0.0054

0.0485

0.0901

0.33

0.018

0.5143

0.1073

0.7216

 

GSZ-score

0.1

0.3

0.0067

0.1327

0.0904

0.3466

0.0156

0.4358

0.1043

0.5892

 

GSZ-score

0.2

0.3

0.0076

0.1892

0.0904

0.3427

0.0128

0.2623

0.1007

0.3901

 

GSZ-score

0.5

0.3

0.0088

0.2547

0.09

0.2874

0.0041

-0.1618

0.0895

0.0725

 

GSZ-score

0

0.5

0.0054

0.0485

0.0901

0.33

0.018

0.5143

0.1073

0.7216

X

GSZ-score

0.1

0.5

0.0081

0.2245

0.09

0.309

0.018

0.5787

0.1049

0.5659

X

GSZ-score

0.2

0.5

0.0086

0.2609

0.0898

0.2778

0.0154

0.3876

0.1016

0.3828

X

GSZ-score

0.5

0.5

0.0094

0.2919

0.0892

0.2115

0.0077

-0.019

0.0917

0.1538

X

t-test

0

 

-0.0165

-0.2968

-0.2707

-0.7744

0.0056

-0.1683

-0.2454

-0.8415

 

t-test

0.1

 

-0.0143

-0.2494

-0.2646

-0.7142

0.0074

-0.0679

-0.2385

-0.7783

 

t-test

0.3

 

-0.0121

-0.2058

-0.2546

-0.6561

0.0087

0.005

-0.2275

-0.7206

 

t-test

1

 

-0.0094

-0.1588

-0.2324

-0.6022

0.01

0.0675

-0.204

-0.6582

X

t-test

3

 

-0.0079

-0.1024

-0.2065

-0.5425

0.0106

0.1249

-0.1775

-0.5926

X

KS

  

-0.0001

-0.0268

0.0462

0.0027

-0.062

-0.5063

-0.0439

-0.3352

X

modKS

  

-0.0164

-0.1558

-0.0231

-0.3216

-0.0449

-0.5979

-0.076

-0.4214

X

iGA

  

0.0075

0.0911

0.0951

0.2485

-0.0058

-0.453

0.0827

-0.0325

X

  1. Table shows average performance of various methods and parameters with artificial datasets. Scores A and B are explained in the main text. The five best methods are highlighted with bold and underlined in each column. The next five methods are underlined. Five weakest scores are represented in italics. Scores obtained with fixed class size represent the performance when the results are normalized with class specific permutation, whereas the results with varying class size show the performance without any normalization.
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BMC Bioinformatics

ISSN: 1471-2105

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