Statistics and informatics in molecular cancer research
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Oxford Univ. Press
2009
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245 | 1 | 0 | |a Statistics and informatics in molecular cancer research |c ed. by Carsten Wiuf ... |
264 | 1 | |a Oxford [u.a.] |b Oxford Univ. Press |c 2009 | |
300 | |a XII, 196 S. |b Ill., graph. Darst. |c 24 cm | ||
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653 | |a Cancer |a Research |a Statistical methods | ||
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Datensatz im Suchindex
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adam_text | Titel: Statistics and informatics in cancer research
Autor: Wiuf, Carsten
Jahr: 2009
I CONTENTS
Preface x
References xii
1 Association studies 1
Emily Webb and Richard Houlston
1.1 Introduction 1
1.2 Sequence variation and patterns of linkage
disequilibrium in the genome 2
1.3 Direct and indirect association studies 4
1.4 Preliminary analysis and quality control 5
1.4.1 Assessment of call rates 5
1.4.2 Duplicate samples 6
1.4.3 Relatedness between study subjects 6
1.4.4 Hardy Weinberg equilibrium 6
1.4.5 Quantile-quantile plots 7
1.5 Techniques for detecting association 7
1.5.1 Single locus tests 7
1.5.2 Incorporating covariates 9
1.5.3 Multi-locus tests 10
1.5.4 Interactive and additive effects 11
1.5.5 Pathway analysis 12
1.5.6 Subgroup analysis 12
1.5.7 Imputation of genotypes 13
1.5.8 Confounding and stratification 13
1.6 Statistical power and multiple testing 14
1.6.1 Design strategies for increasing power 16
1.6.2 The staged design 17
1.7 Replication, quantification, and identification of
causal variants 17
1.8 Discussion 18
1.9 URLs 19
References 20
2 Methods for DNA copy number derivations 25
Cameron Brennan
2.1 Copy number aberration in cancer 25
2.2 Obtaining and analysing copy number data: platforms and
initial processing 25
2.2.1 Array-CGH 26
vi Contents
2.2.2 Oligonucleotide arrays 26
2.2.3 Representational methods 28
2.2.4 Digital karyotyping and sequencing-based approaches 28
2.3 Choosing a platform: array resolution and practical
considerations 29
2.4 Segmentation 31
2.4.1 Artifacts 33
2.5 Aberration types 34
2.5.1 Regional and focal aberrations 34
2.5.2 Copy number variation 36
2.5.3 Regional/broad CNA 37
2.5.4 Focal CNA 37
2.6 Assigning significance to CNA 39
2.7 Breakpoints/translocations 44
2.8 Clustering approaches 46
2.9 Conclusion 48
References 48
3 Methods for derivation of LOH and allelic copy numbers
using SNP arrays 52
Carsten Wiuf, Philippe Larny and Claus L. Andersen
3.1 Introduction 52
3.1.1 Overview 53
3.1.2 Retinoblastoma 53
3.1.3 Identification of TSGs 54
3.1.4 Mechanisms causing AI (in particular LOH) 54
3.1.5 Genomic alterations and their relation to clinical
end-points 55
3.2 Experimental determination of LOH 56
3.3 SNP genotyping arrays 57
3.3.1 Normalization 57
3.3.2 Genotyping 58
3.4 Simple computational tools to infer LOH 60
3.4.1 Classification of genotypes 60
3.4.2 Regions with same boundary (RSB) 60
3.4.3 Nearest Neighbour (NN) 61
3.5 Advanced statistical tools for LOH inference 61
3.5.1 Hidden Markov models 61
3.5.2 Example 63
3.5.3 Two main problems 65
3.5.4 An interpretation of the hidden Markov model 65
3.5.5 Limitations to the HMM approach 65
3.6 Estimation of allele specific copy numbers 67
3.6.1 An allele specific HMM 68
3.6.2 Normalization 68
I
Contents vii
3.6.3 The states 70
3.6.4 Example 70
3.7 Conclusion 74
References 74
4 Bioinformatics of gene expression and copy number data integration 78
Outi Monni and Sampsa Hautaniemi
4.1 Introduction 78
4.2 Methods 79
4.2.1 Methods to study copy number levels 79
4.2.2 Methods to study gene expression 80
4.2.3 Microarrays in detection of copy number and gene
expression levels 81
4.3 Microarray experiment 81
4.4 Analysis and integration of gene expression and copy number
data 87
4.4.1 Preprocessing 87
4.4.2 Identifying amplified and deleted regions from
array-CGH data 89
4.4.3 Statistical approach to integrate gene expression and
array-CGH data 90
4.4.4 Data reduction model approach to integrate gene
expression and array-CGH data 94
4.4.5 Interpolation 96
4.4.6 Gene annotation 97
4.5 Conclusions 97
References 98
5 Analysis of DNA methylation in cancer 102
Fabian Model, Jorn Lewin, Catherine Lofton-Day and
Gunter Weiss
5.1 Introduction 102
5.1.1 DNA methylation biology 102
5.1.2 DNA methylation in cancer 103
5.1.3 Overview 105
5.2 Measuring DNA methylation 105
5.2.1 Measurement technologies 105
5.2.2 Quantification of DNA methylation 108
5.3 Data preprocessing 109
5.3.1 Direct bisulphite sequencing 110
5.3.2 DNA microarrays 114
5.4 Data analysis 118
5.4.1 Tissue classification using DNA microarrays 118
5.4.2 Plasma based cancer detection 123
5.4.3 Cancer recurrence prediction 126
viii Contents
5.5 Conclusions 128
References 128
6 Pathway analysis: Pathway signatures and classification 132
Ming Yi and Robert M. Stephens
6.1 Overview of pathway analysis 132
6.1.1 Pathway and network visualization methods 132
6.1.2 Gene-set based methods 136
6.2 Prom gene signatures/classifiers to pathway
signatures/classifiers 138
6.2.1 Gene signature and classifiers 138
6.2.2 Pathway signatures/classifiers as an alternative? 140
6.2.3 Current advances in pathway-level signatures and
pathway classification 142
6.3 Potentials of pathway-based analysis for integrative discovery 147
6.4 Conclusions 151
References 152
7 Two methods for comparing genomic data across independent
studies in cancer research: Meta-analysis and oncomine
concepts map 160
Wendy Lockwood Banka, Matthew J. Anstett and Daniel R. Rhodes
7.1 Introduction 160
7.2 Single-study gene expression analyses in oncomine 161
7.2.1 Differential expression analysis 161
7.2.2 Co-expression analysis 163
7.3 Meta-analysis 164
7.4 Application 164
7.5 Oncomine concepts map 167
7.5.1 Assembling gene signatures 167
7.5.2 Association analysis 168
7.6 Application 169
7.6.1 Direct comparison of oncomine concepts
results to meta-analysis results 169
7.7 Conclusion 174
References 174
8 Bioinformatic approaches to the analysis of alternative
splicing variants in cancer biology 177
Lue Ping Zhao, Jessica Andriesen and Wenhong Fan
8.1 Introduction to alternative splicing 177
8.1.1 Traditional methods for splicing analysis 177
8.1.2 Current estimates of alternative splicing in humans 179
8.1.3 Alternative splicing and cancer 179
Contents ix
8.2 Oligonucleotide arrays for detecting alternative
splicing variants 179
8.2.1 cDNA arrays 180
8.2.2 GeneChip arrays 180
8.2.3 GeneChip exon arrays 181
8.2.4 Tiling arrays 181
8.3 Bioinformatic approaches 182
8.3.1 Two group design 182
8.3.2 Functional alternative splicing variants utilizing exon
arrays 183
8.3.3 A general framework 184
8.3.4 Relative versus absolute abundance 186
8.3.5 Detection limits 187
8.4 An example 187
8.5 Future directions 189
References 190
Index 193
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language | English |
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physical | XII, 196 S. Ill., graph. Darst. 24 cm |
publishDate | 2009 |
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publisher | Oxford Univ. Press |
record_format | marc |
spellingShingle | Statistics and informatics in molecular cancer research Datenverarbeitung Molekulare Bioinformatik (DE-588)4531334-9 gnd Krebs Medizin (DE-588)4073781-0 gnd |
subject_GND | (DE-588)4531334-9 (DE-588)4073781-0 (DE-588)4143413-4 |
title | Statistics and informatics in molecular cancer research |
title_auth | Statistics and informatics in molecular cancer research |
title_exact_search | Statistics and informatics in molecular cancer research |
title_full | Statistics and informatics in molecular cancer research ed. by Carsten Wiuf ... |
title_fullStr | Statistics and informatics in molecular cancer research ed. by Carsten Wiuf ... |
title_full_unstemmed | Statistics and informatics in molecular cancer research ed. by Carsten Wiuf ... |
title_short | Statistics and informatics in molecular cancer research |
title_sort | statistics and informatics in molecular cancer research |
topic | Datenverarbeitung Molekulare Bioinformatik (DE-588)4531334-9 gnd Krebs Medizin (DE-588)4073781-0 gnd |
topic_facet | Datenverarbeitung Molekulare Bioinformatik Krebs Medizin Aufsatzsammlung |
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