nalyse the bacterial and macrophage data

238

ary

243

Expression Pattern Discovery

245

fferentially expressed genes

245

.1 The biological significance

246

.2 The statistical significance

248

.3 The Type I and Type II errors

249

croarray gene expression analysis

251

.1 The limma package

252

.2 The visualisation of the discovered DEGs using the MA

plot

256

.3 The visualisation of the discovered DEGs using the

volcano plot

257

.4 How to discover DEGs using the limma package

260

EG discovery for RNA-seq sequencing count data

261

.1 Discover DEGs for sequencing count data using DESeq2

262

.2 Discover DEGs for sequencing count data using edgeR

265

scover differentially expressed genes when outliers are present

268

.1 Example of heterogeneous gene expression

268

.2 COPA

271

.3 OS

272

.4 ORT

272

.5 MOST

272

.6 LSOSS

273

.7 DOG

273

.8 Discover DEGs when outlier genes are present —

simulated data

278

.9 Discover heterogenous DEGs for a cancer data set

282

ne expression bimodality pattern discovery

288

.1 The likelihood ratio test approach

289

.2 The bimodality index test approach

290

.3 The gap maximisation test approach

291

.4 Simulated data analysis

298

.5 Letrozole data analysis

300

ual-scale Gaussian model for small replicate data DEG

covery

302

.1 The dual-scale Gaussian model

302

6.6.1.1 The working principle of DSG

302

6.6.1.2 DSG for simulated data DEG discovery

306