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Table 2 Comparison of screening and clustering results (high 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

.326

.259

.200

.235

.292

.149

.589

.708

.411

.291

3

.321

.192

.199

.298

.214

.151

.561

.716

.439

.283

4

.321

.151

.194

.320

.166

.156

.553

.722

.447

.278

5

.323

.125

.185

.269

.142

.156

.571

.714

.428

.285

8

.324

.084

.175

.324

.094

.148

.552

.722

.448

.277

GPR**

     

.483

.779

.221

.517

p = 0.2

FC

2

.343

.253

.164

.234

.291

.117

.567

.697

.432

.302

3

.339

.185

.155

.287

.208

.117

.545

.703

.454

.296

4

.338

.149

.151

.306

.160

.120

.539

.708

.461

.291

5

.337

.125

.146

.261

.138

.120

.555

.702

.445

.297

8

.338

.086

.132

.307

.094

.113

.538

.706

.462

.293

GPR

     

.536

.677

.323

.463

p = 0.3

FC

2

.359

.248

.128

.284

.290

.090

.546

.681

.453

.318

3

.351

.185

.119

.329

.208

.089

.531

.683

.468

.316

4

.350

.148

.115

.347

.159

.092

.526

.685

.473

.314

5

.350

.127

.108

.304

.137

.088

.537

.681

.462

.318

8

.357

.083

.089

.351

.091

.079

.526

.685

.474

.314

GPR

     

.584

.572

.427

.415

p = 0.5

FC

2

.383

.246

.073

.330

.284

.053

.517

.632

.482

.367

3

.375

.183

.066

.356

.198

.051

.512

.634

.488

.365

4

.369

.151

.062

.365

.158

.052

.510

.633

.490

.366

5

.369

.126

.056

.338

.137

.046

.514

.633

.485

.367

8

.370

.086

.046

.370

.092

.042

.509

.634

.490

.365

GPR

     

.646

.409

.590

.353

p = 0.7

FC

2

.395

.248

.035

.356

.275

.030

.505

.504

.495

.422

3

.384

.186

.034

.368

.193

.028

.503

.503

.496

.424

4

.383

.148

.031

.373

.155

.026

.502

.502

.497

.421

5

.381

.125

.028

.358

.134

.024

.504

.504

.496

.419

8

.377

.092

.027

.370

.097

.023

.503

.502

.497

.415

 

GPR

     

.679

.337

.662

.320

  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 = 1.5.
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BMC Bioinformatics

ISSN: 1471-2105

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