Microarray gene expression data analysis : a beginner's guide /
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作 者：Helen C. Causton, John Quackenbush, and Alvis Brazma.
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ISBN：9781405106825
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简介
Summary:
Publisher Summary 1
Emphasizing underlying concepts and principles, this concise guide describes the design of microarray experiments and the analysis of the data they produce. Intended for graduate students and bioinformatics researchers, the book stresses the conceptual aspects of analysis and keeps mathematical complexities to a minimum. The authors are scientists with the University of London, The Institute for Genomic Research, and the European Bioinformatics Institute. Annotation (c) Book News, Inc., Portland, OR (booknews.com)
Publisher Summary 2
This guide covers aspects of designing microarray experiments and analysing the data generated, including information on some of the tools that are available from non-commercial sources. Concepts and principles underpinning gene expression analysis are emphasised and wherever possible, the mathematics has been simplified. The guide is intended for use by graduates and researchers in bioinformatics and the life sciences and is also suitable for statisticians who are interested in the approaches currently used to study gene expression.Microarrays are an automated way of carrying out thousands of experiments at once, and allows scientists to obtain huge amounts of information very quicklyShort, concise text on this difficult topic areaClear illustrations throughoutWritten by well-known teachers in the subjectProvides insight into how to analyse the data produced from microarrays
目录
Preface p. ix
Acknowledgements p. xi
Introduction
The central dogma of molecular biology p. 3
What are microarrays and how do they work? p. 4
Gene function and drug discovery p. 5
Data generation, processing and analysis: an overview p. 7
Data management p. 10
References p. 12
Experimental design
Experimental objectives and features of microarray data p. 14
General principles of experimental design p. 16
Reducing the number of variables p. 17
Time courses vs. independent data points p. 18
Replicates and repeated measurements p. 18
Reference samples p. 22
Exogenous ('spiked-in') controls p. 23
Dual labelling/dye swapping p. 24
Validation of results p. 25
Choice and preparation of samples p. 26
Obtaining the appropriate sample p. 26
Strain background p. 26
Mutants p. 26
Reagents p. 27
Sample and sample composition p. 27
Small amounts of sample: RNA amplification and pooling p. 28
Preparation of the labelled extract p. 28
Assessing the quality of the labelled extract p. 29
Choice and design of arrays p. 30
Choice of array platform p. 30
Oligonucleotides vs. PCR products p. 31
Replicate, guide and control features p. 32
Cross-hybridisation p. 32
Hybridisation, scanning and quality control p. 33
Long-term considerations p. 35
Record keeping p. 35
Standardisation p. 36
References p. 37
Image processing, normalisation and data transformation
Introduction p. 40
Preliminary processing of the data p. 41
Image analysis p. 41
Measuring and reporting expression p. 42
Saturated pixels p. 44
The appropriate number of pixels p. 44
Estimating background p. 46
Reporting expression with Affymetrix GeneChips p. 47
Expression ratios: the starting point for sample comparison p. 48
Transformations of the expression ratio p. 49
Situations where expression does not correlate with spot intensity p. 51
Normalisation p. 51
Total intensity normalisation p. 54
Mean log centring p. 55
Linear regression p. 56
Chen's ratio statistics p. 57
Lowess normalisation p. 57
Global vs. local normalisation p. 59
Data filtering p. 60
Filtering low intensity data p. 61
Setting floors and ceilings p. 61
Use of replicate data p. 62
Experimental design strategies p. 62
Replicate filtering p. 63
Averaging replicate data p. 64
Identification of differentially expressed genes p. 66
Intensity-dependent estimation of differential expression p. 66
Analysis of variance p. 67
References p. 69
Analysis of gene expression data matrices
Introduction p. 71
Gene expression data matrices: their features and representations p. 75
Gene expression matrices p. 75
Representation of expression data as vector space--sample space and gene space p. 79
Distance and similarity measures in expression space p. 81
Euclidean, Minkowski, Manhattan, angle and chord distances p. 82
Pearson correlation distance, adjusting the mean and variance, correlation matrices, and the relationship between Euclidean and correlation distances p. 85
Spearman's rank correlation p. 89
Distances in discretised space, and mutual information p. 90
Principal component analysis, eigen-vectors and eigen-genes p. 92
Dealing with missing values p. 94
Representation of gene expression data by graphs (networks) p. 95
Gene expression matrix annotation p. 95
Clustering p. 98
Types of clustering p. 99
Hierarchical agglomerative clustering p. 100
Hierarchical divisive clustering p. 103
Non-hierarchical clustering--K-means p. 104
Self-organising maps and trees p. 105
Relationship between clustering and PCA p. 106
'Gene shaving' p. 107
Clustering in discretised space p. 108
Graph-based clustering p. 108
Bayesian or model-based clustering and fuzzy clustering p. 109
Clustering genes and samples--applications of clustering p. 110
Cluster scoring and validation p. 112
Classification algorithms and class prediction p. 113
Definition of the problem p. 114
Linear discriminants p. 115
Support vector machines p. 117
K-nearest neighbour method p. 118
Neural networks, decision trees and applications of classification p. 119
Partially supervised analysis p. 120
Class discovery p. 120
Time series analysis p. 121
Visualisation p. 124
Downstream from expression profile analysis p. 126
Identification of regulatory signals p. 128
References p. 130
Non-commercial software p. 134
Statistical analysis p. 134
Normalisation, clustering and classification p. 136
Visualisation p. 139
Multifunctional software p. 139
References p. 141
Glossary p. 143
Index p. 154
Acknowledgements p. xi
Introduction
The central dogma of molecular biology p. 3
What are microarrays and how do they work? p. 4
Gene function and drug discovery p. 5
Data generation, processing and analysis: an overview p. 7
Data management p. 10
References p. 12
Experimental design
Experimental objectives and features of microarray data p. 14
General principles of experimental design p. 16
Reducing the number of variables p. 17
Time courses vs. independent data points p. 18
Replicates and repeated measurements p. 18
Reference samples p. 22
Exogenous ('spiked-in') controls p. 23
Dual labelling/dye swapping p. 24
Validation of results p. 25
Choice and preparation of samples p. 26
Obtaining the appropriate sample p. 26
Strain background p. 26
Mutants p. 26
Reagents p. 27
Sample and sample composition p. 27
Small amounts of sample: RNA amplification and pooling p. 28
Preparation of the labelled extract p. 28
Assessing the quality of the labelled extract p. 29
Choice and design of arrays p. 30
Choice of array platform p. 30
Oligonucleotides vs. PCR products p. 31
Replicate, guide and control features p. 32
Cross-hybridisation p. 32
Hybridisation, scanning and quality control p. 33
Long-term considerations p. 35
Record keeping p. 35
Standardisation p. 36
References p. 37
Image processing, normalisation and data transformation
Introduction p. 40
Preliminary processing of the data p. 41
Image analysis p. 41
Measuring and reporting expression p. 42
Saturated pixels p. 44
The appropriate number of pixels p. 44
Estimating background p. 46
Reporting expression with Affymetrix GeneChips p. 47
Expression ratios: the starting point for sample comparison p. 48
Transformations of the expression ratio p. 49
Situations where expression does not correlate with spot intensity p. 51
Normalisation p. 51
Total intensity normalisation p. 54
Mean log centring p. 55
Linear regression p. 56
Chen's ratio statistics p. 57
Lowess normalisation p. 57
Global vs. local normalisation p. 59
Data filtering p. 60
Filtering low intensity data p. 61
Setting floors and ceilings p. 61
Use of replicate data p. 62
Experimental design strategies p. 62
Replicate filtering p. 63
Averaging replicate data p. 64
Identification of differentially expressed genes p. 66
Intensity-dependent estimation of differential expression p. 66
Analysis of variance p. 67
References p. 69
Analysis of gene expression data matrices
Introduction p. 71
Gene expression data matrices: their features and representations p. 75
Gene expression matrices p. 75
Representation of expression data as vector space--sample space and gene space p. 79
Distance and similarity measures in expression space p. 81
Euclidean, Minkowski, Manhattan, angle and chord distances p. 82
Pearson correlation distance, adjusting the mean and variance, correlation matrices, and the relationship between Euclidean and correlation distances p. 85
Spearman's rank correlation p. 89
Distances in discretised space, and mutual information p. 90
Principal component analysis, eigen-vectors and eigen-genes p. 92
Dealing with missing values p. 94
Representation of gene expression data by graphs (networks) p. 95
Gene expression matrix annotation p. 95
Clustering p. 98
Types of clustering p. 99
Hierarchical agglomerative clustering p. 100
Hierarchical divisive clustering p. 103
Non-hierarchical clustering--K-means p. 104
Self-organising maps and trees p. 105
Relationship between clustering and PCA p. 106
'Gene shaving' p. 107
Clustering in discretised space p. 108
Graph-based clustering p. 108
Bayesian or model-based clustering and fuzzy clustering p. 109
Clustering genes and samples--applications of clustering p. 110
Cluster scoring and validation p. 112
Classification algorithms and class prediction p. 113
Definition of the problem p. 114
Linear discriminants p. 115
Support vector machines p. 117
K-nearest neighbour method p. 118
Neural networks, decision trees and applications of classification p. 119
Partially supervised analysis p. 120
Class discovery p. 120
Time series analysis p. 121
Visualisation p. 124
Downstream from expression profile analysis p. 126
Identification of regulatory signals p. 128
References p. 130
Non-commercial software p. 134
Statistical analysis p. 134
Normalisation, clustering and classification p. 136
Visualisation p. 139
Multifunctional software p. 139
References p. 141
Glossary p. 143
Index p. 154
Microarray gene expression data analysis : a beginner's guide /
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