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Table 1 Comparison of screening and clustering results (low noise)

From: A method to identify differential expression profiles of time-course gene data with Fourier transformation

   

Without screening

With screening

AR(1) parameter

Method

J

Error

Sil

ARI

Error

Sil

ARI

Sensitivity

Specificity

FDR

FNR

p = 0.1

FC*

2

.037

.509

.909

.015

.560

.918

.878

.723

.121

.276

 

3

.020

.471

.921

.016

.484

.932

.860

.783

.139

.216

 

4

.015

.430

.963

.017

.438

.937

.863

.842

.136

.157

 

5

.015

.388

.964

.014

.403

.944

.854

.798

.145

.201

 

8

.015

.305

.964

.017

.317

.940

.851

.836

.148

.163

GPR**

   

.855

.779

.220

.144

p = 0.2

FC

2

.052

.471

.871

.021

.523

.875

.871

.722

.128

.277

 

3

.036

.423

.912

.026

.443

.888

.846

.783

.153

.217

 

4

.029

.386

.931

.028

.398

.895

.847

.839

.152

.160

 

5

.027

.348

.935

.022

.366

.906

.837

.798

.162

.205

 

8

.028

.274

.936

.029

.287

.895

.830

.836

.169

.163

GPR

   

.826

678

.321

.173

p = 0.3

FC

2

.073

.430

.822

.030

.487

.815

.863

.723

.136

.276

 

3

.056

.380

.865

.042

.402

.814

.828

.783

.171

.217

 

4

.052

.339

.875

.045

.356

.825

.827

.834

.172

.165

 

5

.049

.306

.883

.036

.326

.845

.817

.790

.182

.209

 

8

.047

.244

.888

.049

.257

.823

.803

.832

.196

.167

GPR

   

.798

.571

.428

.201

p = 0.5

FC

2

.159

.340

.610

.056

.414

.633

.835

.717

.165

.201

 

3

.139

.287

.663

.093

.329

.591

.775

.768

.224

.231

 

4

.124

.255

.702

.113

.280

.578

.766

.811

.233

.188

 

5

.132

.226

.682

.093

.259

.615

.762

.773

.237

.226

 

8

.143

.181

.649

.134

.205

.562

.730

.815

.269

.184

GPR

   

.756

.410

.589

.244

p = 0.7

FC

2

.266

.287

.345

.088

.357

.351

.755

.704

.244

.295

 

3

.264

.224

.347

.153

.272

.314

.682

.738

.317

.261

 

4

.258

.190

.370

.186

.230

.303

.668

.771

.331

.228

 

5

.258

.171

.375

.161

.211

.317

.676

.745

.324

.255

 

8

.267

.137

.339

.220

.172

.287

.641

.769

.358

.230

 

GPR

   

.731

.335

.664

.268

  1. * FC: proposed method with Fourier coefficients, **GPR: Gaussian process regression.
  2. Comparison of estimation error rate (E), Silhouette width (S) and Adjusted Rand Index (ARI) values of model-based clustering without screening vs with screening with J Fourier coefficients including sensitivity, specificity, FDR and FNR with m = 20 time points. These summaries are based on 500 repetitions of each consisting of 800 curves with AR(1) parameter ρ’s with the noise standard deviation σ = 0.5.
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BMC Bioinformatics

ISSN: 1471-2105

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