Statistics for Microarrays : Design, Analysis and Inference

✍ Scribed by Ernst Wit, John McClure

• Year: 2004
• Tongue: English
• Leaves: 280
• Edition: 1
• Category: Library

✦ Synopsis

Interest in microarrays has increased considerably in the last ten years. This increase in the use of microarray technology has led to the need for good standards of microarray experimental notation, data representation, and the introduction of standard experimental controls, as well as standard data normalization and analysis techniques. Statistics for Microarrays: Design, Analysis and Inference is the first book that presents a coherent and systematic overview of statistical methods in all stages in the process of analysing microarray data – from getting good data to obtaining meaningful results.Provides an overview of statistics for microarrays, including experimental design, data preparation, image analysis, normalization, quality control, and statistical inference.Features many examples throughout using real data from microarray experiments.Computational techniques are integrated into the text.Takes a very practical approach, suitable for statistically-minded biologists.Supported by a Website featuring colour images, software, and data sets.Primarily aimed at statistically-minded biologists, bioinformaticians, biostatisticians, and computer scientists working with microarray data, the book is also suitable for postgraduate students of bioinformatics.

✦ Table of Contents

Contents......Page 10
Preface......Page 14
1.1 Using the R Computing Environment......Page 16
1.1.1 Installing smida......Page 17
1.2 Data Sets from Biological Experiments......Page 18
1.2.1 Arabidopsis experiment: Anna Amtmann......Page 19
1.2.2 Skin cancer experiment: Nighean Barr......Page 21
1.2.3 Breast cancer experiment: John Bartlett......Page 22
1.2.4 Mammary gland experiment: Gusterson group......Page 24
1.2.5 Tuberculosis experiment: BμG@S group......Page 25
I: Getting Good Data......Page 28
2.1 Nucleic Acids: DNA and RNA......Page 30
2.2 Simple cDNA Spotted Microarray Experiment......Page 31
3 Statistical Design of Microarrays......Page 38
3.1 Sources of Variation......Page 39
3.2 Replication......Page 41
3.3 Design Principles......Page 51
3.4 Single-channel Microarray Design......Page 55
3.5 Two-channel Microarray Designs......Page 59
4.1 Image Analysis......Page 72
4.2 Introduction to Normalization......Page 77
4.3 Normalization for Dual-channel Arrays......Page 84
4.4 Normalization of Single-channel Arrays......Page 108
5 Quality Assessment......Page 118
5.1 Using MIAME in Quality Assessment......Page 119
5.2 Comparing Multivariate Data......Page 120
5.3 Detecting Data Problems......Page 128
5.4 Consequences of Quality Assessment Checks......Page 138
6.1 Design......Page 140
6.2 Normalization......Page 144
II: Getting Good Answers......Page 150
7.1 Discovering Sample Classes......Page 152
7.2 Exploratory Supervised Learning......Page 170
7.3 Discovering Gene Clusters......Page 175
8.1 Introduction......Page 192
8.2 Classical Hypothesis Testing......Page 194
8.3 Bayesian Hypothesis Testing......Page 211
9.1 Introduction......Page 226
9.2 Curse of Dimensionality: Gene Filtering......Page 233
9.3 Predicting Class Memberships......Page 238
9.4 Predicting Continuous Responses......Page 250
10.1 Differential Expression......Page 262
10.2 Prediction and Learning......Page 264
Bibliography......Page 266
B......Page 274
D......Page 275
G......Page 276
L......Page 277
P......Page 278
S......Page 279
W......Page 280