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E-raamat: Statistics for Microarrays: Design, Analysis and Inference

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(University of Glasgow, UK), (University of Glasgow, UK)
  • Formaat: PDF+DRM
  • Ilmumisaeg: 19-Nov-2004
  • Kirjastus: John Wiley & Sons Inc
  • Keel: eng
  • ISBN-13: 9780470011072
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Statistics for Microarrays: Design, Analysis and InferenceSuurem pilt
  • Formaat: PDF+DRM
  • Ilmumisaeg: 19-Nov-2004
  • Kirjastus: John Wiley & Sons Inc
  • Keel: eng
  • ISBN-13: 9780470011072
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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.

Arvustused

"I liked this book and would recommend it to any statistician new to microarray data analysisa unique combination of features that make it a contender among the standard textbooks" (Journal of the American Statistical Association, June 2006) "...clear...up-to-date...lively advice...an excellent reference text for any researcher interested in the analysis of transcriptomic data." (Short Book Reviews, Vol.25, No.1, April 2005)

"...this is a very good introduction to one of the most widely used methods for assessing differential expression..." (Journal of the Royal Statistical Society, Vol 168 (4) 2005)

"...presents a coherent and systematic overview of statistical methods in all stages of the process of analysing microarray data..." (Zentralblatt Math, Vol.1049, 2004)

Preface xi
1 Preliminaries 1(112)
1.1 Using the R Computing Environment
1(2)
1.1.1 Installing smida
2(1)
1.1.2 Loading smida
3(1)
1.2 Data Sets from Biological Experiments
3(132)
1.2.1 Arabidopsis experiment: Anna Amtmann
4(2)
1.2.2 Skin cancer experiment: Nighean Barr
6(1)
1.2.3 Breast cancer experiment: John Bartlett
7(2)
1.2.4 Mammary gland experiment: Gusterson group
9(1)
1.2.5 Tuberculosis experiment: ΒμG@S group
10(103)
I Getting Good Data 113(22)
2 Set-up of a Microarray Experiment
15(8)
2.1 Nucleic Acids: DNA and RNA
15(1)
2.2 Simple cDNA Spotted Microarray Experiment
16(7)
2.2.1 Growing experimental material
17(1)
2.2.2 Obtaining RNA
17(1)
2.2.3 Adding spiking RNA and poly-T primer
18(1)
2.2.4 Preparing the enzyme environment
19(1)
2.2.5 Obtaining labelled cDNA
19(1)
2.2.6 Preparing cDNA mixture for hybridization
19(1)
2.2.7 Slide hybridization
20(3)
3 Statistical Design of Microarrays
23(34)
3.1 Sources of Variation
24(2)
3.2 Replication
26(10)
3.2.1 Biological and technical replication
27(2)
3.2.2 How many replicates?
29(1)
3.2.3 Pooling samples
30(6)
3.3 Design Principles
36(4)
3.3.1 Blocking, crossing and randomization
37(2)
3.3.2 Design and normalization
39(1)
3.4 Single-channel Microarray Design
40(4)
3.4.1 Design issues
41(1)
3.4.2 Design layout
42(1)
3.4.3 Dealing with technical replicates
42(2)
3.5 Two-channel Microarray Designs
44(13)
3.5.1 Optimal design of dual-channel arrays
44(6)
3.5.2 Several practical two-channel designs
50(7)
4 Normalization
57(46)
4.1 Image Analysis
57(5)
4.1.1 Filtering
58(2)
4.1.2 Gridding
60(1)
4.1.3 Segmentation
61(1)
4.1.4 Quantification
62(1)
4.2 Introduction to Normalization
62(7)
4.2.1 Scale of gene expression data
63(2)
4.2.2 Using control spots for normalization
65(1)
4.2.3 Missing data
65(4)
4.3 Normalization for Dual-channel Arrays
69(24)
4.3.1 Order for the normalizations
70(1)
4.3.2 Spatial correction
71(5)
4.3.3 Background correction
76(4)
4.3.4 Dye effect normalization
80(4)
4.3.5 Normalization within and across conditions
84(9)
4.4 Normalization of Single-channel Arrays
93(10)
4.4.1 Affymetrix data structure
93(1)
4.4.2 Normalization of Affymetrix data
94(9)
5 Quality Assessment
103(22)
5.1 Using MIAME in Quality Assessment
104(1)
5.1.1 Components of MIAME
104(1)
5.2 Comparing Multivariate Data
105(8)
5.2.1 Measurement scale
105(1)
5.2.2 Dissimilarity and distance measures
106(5)
5.2.3 Representing multivariate data
111(2)
5.3 Detecting Data Problems
113(10)
5.3.1 Clerical errors
114(3)
5.3.2 Normalization problems
117(2)
5.3.3 Hybridization problems
119(2)
5.3.4 Array mishandling
121(2)
5.4 Consequences of Quality Assessment Checks
123(2)
6 Microarray Myths: Data
125(12)
6.1 Design
125(4)
6.1.1 Single-versus dual-channel designs?
125(4)
6.1.2 Dye-swap experiments
129(1)
6.2 Normalization
129(8)
6.2.1 Myth: 'microarray data is Gaussian'
129(2)
6.2.2 Myth: 'microarray data is not Gaussian'
131(1)
6.2.3 Confounding spatial and dye effect
132(1)
6.2.4 Myth: 'non-negative background subtraction'
133(2)
II Getting Good Answers 135(116)
7 Microarray Discoveries
137(40)
7.1 Discovering Sample Classes
137(18)
7.1.1 Why cluster samples?
138(1)
7.1.2 Sample dissimilarity measures
139(5)
7.1.3 Clustering methods for samples
144(11)
7.2 Exploratory Supervised Learning
155(5)
7.2.1 Labelled dendrograms
156(1)
7.2.2 Labelled PAM-type clusterings
157(3)
7.3 Discovering Gene Clusters
160(17)
7.3.1 Similarity measures for expression profiles
160(3)
7.3.2 Gene clustering methods
163(14)
8 Differential Expression
177(34)
8.1 Introduction
177(2)
8.1.1 Classical versus Bayesian hypothesis testing
177(2)
8.1.2 Multiple testing 'problem'
179(1)
8.2 Classical Hypothesis Testing
179(17)
8.2.1 What is a hypothesis test?
180(3)
8.2.2 Hypothesis tests for two conditions
183(9)
8.2.3 Decision rules
192(3)
8.2.4 Results from skin cancer experiment
195(1)
8.3 Bayesian Hypothesis Testing
196(15)
8.3.1 A general testing procedure
200(3)
8.3.2 Bayesian τ-test
203(8)
9 Predicting Outcomes with Gene Expression Profiles
211(36)
9.1 Introduction
211(7)
9.1.1 Probabilistic classification theory
212(5)
9.1.2 Modelling and predicting continuous variables
217(1)
9.2 Curse of Dimensionality: Gene Filtering
218(5)
9.2.1 Use only significantly expressed genes
218(2)
9.2.2 PCA and gene clustering
220(2)
9.2.3 Penalized methods
222(1)
9.2.4 Biological selection
222(1)
9.3 Predicting Class Memberships
223(12)
9.3.1 Variance-bias trade-off in prediction
223(4)
9.3.2 Linear discriminant analysis
227(4)
9.3.3 kappa-nearest neighbour classification
231(4)
9.4 Predicting Continuous Responses
235(12)
9.4.1 Penalized regression: LASSO
235(8)
9.4.2 kappa-nearest neighbour regression
243(4)
10 Microarray Myths: Inference
247(4)
10.1 Differential Expression
247(2)
10.1.1 Myth: 'Bonferroni is too conservative'
247(1)
10.1.2 FPR and collective multiple testing
248(1)
10.1.3 Misinterpreting FDR
248(1)
10.2 Prediction and Learning
249(2)
10.2.1 Cross-validation
249(2)
Bibliography 251(8)
Index 259


Ernst Wit is the author of Statistics for Microarrays: Design, Analysis and Inference, published by Wiley.

John McClure is the author of Statistics for Microarrays: Design, Analysis and Inference, published by Wiley.
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