Molecular understanding of cancer and cancer progression is at the forefront of many research programs today, and high-throughput array technologies and other modern molecular techniques produce a wealth of molecular data about the stucture, organization, and function of cells, tissues and organisms. Complex mathematical, statistical and bioinformatics tools are required to extract, handle and process data and this book, edited by two leading researchers with contributions from carefully chosen experts, makes these tools available to a wide range of researchers, in a single coherent book volume.
Author(s): Carsten Wiuf, Claus L. Andersen
Edition: 1
Publisher: Oxford University Press, USA
Year: 2009
Language: English
Pages: 217
Contents......Page 6
Preface......Page 11
References......Page 13
1.1 Introduction......Page 14
1.2 Sequence variation and patterns of linkage disequilibrium in the genome......Page 15
1.3 Direct and indirect association studies......Page 17
1.4.1 Assessment of call rates......Page 18
1.4.4 Hardy–Weinberg equilibrium......Page 19
1.5.1 Single locus tests......Page 20
1.5.2 Incorporating covariates......Page 22
1.5.3 Multi-locus tests......Page 23
1.5.4 Interactive and additive effects......Page 24
1.5.6 Subgroup analysis......Page 25
1.5.8 Confounding and stratification......Page 26
1.6 Statistical power and multiple testing......Page 27
1.6.1 Design strategies for increasing power......Page 29
1.7 Replication, quantification, and identification of causal variants......Page 30
1.8 Discussion......Page 31
1.9 URLs......Page 32
References......Page 33
2.2 Obtaining and analysing copy number data: platforms and initial processing......Page 38
2.2.2 Oligonucleotide arrays......Page 39
2.2.4 Digital karyotyping and sequencing-based approaches......Page 41
2.3 Choosing a platform: array resolution and practical considerations......Page 42
2.4 Segmentation......Page 44
2.4.1 Artifacts......Page 46
2.5.1 Regional and focal aberrations......Page 47
2.5.2 Copy number variation......Page 49
2.5.4 Focal CNA......Page 50
2.6 Assigning significance to CNA......Page 52
2.7 Breakpoints/translocations......Page 57
2.8 Clustering approaches......Page 59
References......Page 61
3.1 Introduction......Page 65
3.1.2 Retinoblastoma......Page 66
3.1.4 Mechanisms causing AI (in particular LOH)......Page 67
3.1.5 Genomic alterations and their relation to clinical end-points......Page 68
3.2 Experimental determination of LOH......Page 69
3.3.1 Normalization......Page 70
3.3.2 Genotyping......Page 71
3.4.2 Regions with same boundary (RSB)......Page 73
3.5.1 Hidden Markov models......Page 74
3.5.2 Example......Page 76
3.5.5 Limitations to the HMM approach......Page 78
3.6 Estimation of allele specific copy numbers......Page 80
3.6.2 Normalization......Page 81
3.6.4 Example......Page 83
References......Page 87
4.1 Introduction......Page 91
4.2.1 Methods to study copy number levels......Page 92
4.2.2 Methods to study gene expression......Page 93
4.3 Microarray experiment......Page 94
4.4.1 Preprocessing......Page 100
4.4.2 Identifying amplified and deleted regions from array-CGH data......Page 102
4.4.3 Statistical approach to integrate gene expression and array-CGH data......Page 103
4.4.4 Data reduction model approach to integrate gene expression and array-CGH data......Page 107
4.4.5 Interpolation......Page 109
4.5 Conclusions......Page 110
References......Page 111
5.1.1 DNA methylation biology......Page 115
5.1.2 DNA methylation in cancer......Page 116
5.2.1 Measurement technologies......Page 118
5.2.2 Quantification of DNA methylation......Page 121
5.3 Data preprocessing......Page 122
5.3.1 Direct bisulphite sequencing......Page 123
5.3.2 DNA microarrays......Page 127
5.4.1 Tissue classification using DNA microarrays......Page 131
5.4.2 Plasma based cancer detection......Page 136
5.4.3 Cancer recurrence prediction......Page 139
References......Page 141
6.1.1 Pathway and network visualization methods......Page 145
6.1.2 Gene-set based methods......Page 149
6.2.1 Gene signature and classifiers......Page 151
6.2.2 Pathway signatures/classifiers as an alternative?......Page 153
6.2.3 Current advances in pathway-level signatures and pathway classification......Page 155
6.3 Potentials of pathway-based analysis for integrative discovery......Page 160
6.4 Conclusions......Page 164
References......Page 165
7.1 Introduction......Page 173
7.2.1 Differential expression analysis......Page 174
7.2.2 Co-expression analysis......Page 176
7.4 Application......Page 177
7.5.1 Assembling gene signatures......Page 180
7.5.2 Association analysis......Page 181
7.6.1 Direct comparison of oncomine concepts results to meta-analysis results......Page 182
References......Page 187
8.1.1 Traditional methods for splicing analysis......Page 190
8.2 Oligonucleotide arrays for detecting alternative splicing variants......Page 192
8.2.2 GeneChip arrays......Page 193
8.2.4 Tiling arrays......Page 194
8.3.1 Two group design......Page 195
8.3.2 Functional alternative splicing variants utilizing exon arrays......Page 196
8.3.3 A general framework......Page 197
8.3.4 Relative versus absolute abundance......Page 199
8.4 An example......Page 200
8.5 Future directions......Page 202
References......Page 203
F......Page 206
M......Page 207
S......Page 208
W......Page 209
