Book contents
- Frontmatter
- Contents
- List of Contributors
- Preface
- 1 An Introduction to Next-Generation Biological Platforms
- 2 An Introduction to The Cancer Genome Atlas
- 3 DNA Variant Calling in Targeted Sequencing Data
- 4 Statistical Analysis of Mapped Reads from mRNA-Seq Data
- 5 Model-Based Methods for Transcript Expression-Level Quantification in RNA-Seq
- 6 Bayesian Model-Based Approaches for Solexa Sequencing Data
- 7 Statistical Aspects of ChIP-Seq Analysis
- 8 Bayesian Modeling of ChIP-Seq Data from Transcription Factor to Nucleosome Positioning
- 9 Multivariate Linear Models for GWAS
- 10 Bayesian Model Averaging for Genetic Association Studies
- 11 Whole-Genome Multi-SNP-Phenotype Association Analysis
- 12 Methods for the Analysis of Copy Number Data in Cancer Research
- 13 Bayesian Models for Integrative Genomics
- 14 Bayesian Graphical Models for Integrating Multiplatform Genomics Data
- 15 Genetical Genomics Data: Some Statistical Problems and Solutions
- 16 A Bayesian Framework for Integrating Copy Number and Gene Expression Data
- 17 Application of Bayesian Sparse Factor Analysis Models in Bioinformatics
- 18 Predicting Cancer Subtypes Using Survival-Supervised Latent Dirichlet Allocation Models
- 19 Regularization Techniques for Highly Correlated Gene Expression Data with Unknown Group Structure
- 20 Optimized Cross-Study Analysis of Microarray-Based Predictors
- 21 Functional Enrichment Testing: A Survey of Statistical Methods
- 22 Discover Trend and Progression Underlying High-Dimensional Data
- 23 Bayesian Phylogenetics Adapts to Comprehensive Infectious Disease Sequence Data
- Index
- Plate section
12 - Methods for the Analysis of Copy Number Data in Cancer Research
Published online by Cambridge University Press: 05 June 2013
- Frontmatter
- Contents
- List of Contributors
- Preface
- 1 An Introduction to Next-Generation Biological Platforms
- 2 An Introduction to The Cancer Genome Atlas
- 3 DNA Variant Calling in Targeted Sequencing Data
- 4 Statistical Analysis of Mapped Reads from mRNA-Seq Data
- 5 Model-Based Methods for Transcript Expression-Level Quantification in RNA-Seq
- 6 Bayesian Model-Based Approaches for Solexa Sequencing Data
- 7 Statistical Aspects of ChIP-Seq Analysis
- 8 Bayesian Modeling of ChIP-Seq Data from Transcription Factor to Nucleosome Positioning
- 9 Multivariate Linear Models for GWAS
- 10 Bayesian Model Averaging for Genetic Association Studies
- 11 Whole-Genome Multi-SNP-Phenotype Association Analysis
- 12 Methods for the Analysis of Copy Number Data in Cancer Research
- 13 Bayesian Models for Integrative Genomics
- 14 Bayesian Graphical Models for Integrating Multiplatform Genomics Data
- 15 Genetical Genomics Data: Some Statistical Problems and Solutions
- 16 A Bayesian Framework for Integrating Copy Number and Gene Expression Data
- 17 Application of Bayesian Sparse Factor Analysis Models in Bioinformatics
- 18 Predicting Cancer Subtypes Using Survival-Supervised Latent Dirichlet Allocation Models
- 19 Regularization Techniques for Highly Correlated Gene Expression Data with Unknown Group Structure
- 20 Optimized Cross-Study Analysis of Microarray-Based Predictors
- 21 Functional Enrichment Testing: A Survey of Statistical Methods
- 22 Discover Trend and Progression Underlying High-Dimensional Data
- 23 Bayesian Phylogenetics Adapts to Comprehensive Infectious Disease Sequence Data
- Index
- Plate section
Summary
Introduction
Cancers are fundamentally caused by genomic changes in the cancer cells that lead to their uncontrolled growth (Balmain et al., 2003; Stratton et al., 2009). Understanding these changes, which include DNA copy number alterations, is an intense focus of current research into the causes of, and potential therapies for, every type of cancer. Major research projects, such as the Cancer Genome Atlas (TCGA) project (The Cancer Genome Atlas Research Network, 2008), aim to comprehensively catalog all genomic changes in cancer. This chapter discusses the problem of interpreting copy number data, specifically in the context of cancer research.
To measure copy number, whole-genome genotyping array assays hybridize sample DNA to oligonucleotides deposited on the array. Modern designs use synthetic oligonucleotides to measure copy number at frequent intervals along the genome, especially in regions of known copy number variation. Modern arrays also include many probes that target both alleles of a large number of common single-nucleotide polymorphisms (SNPs). These platforms are therefore widely used in genotyping studies. Array-based assays available for measuring genome-wide copy number include arrays from Illumina, Sentrix, Agilent, and Affymetrix. Data from next-generation sequencing of DNA can also be used to detect copy number alterations and is rapidly becoming cost competitive with array-based platforms.
Molecular inversion probe (MIP) arrays (Wang et al., 2007, 2009; Ji and Welch, 2009) are another platform that can be used for large-scale copy number analysis and genotyping. MIP technology uses less DNA, can handle lower quality DNA, has a greater dynamic range, has higher quality markers, and better separates allelic information than other array-based approaches.
- Type
- Chapter
- Information
- Advances in Statistical BioinformaticsModels and Integrative Inference for High-Throughput Data, pp. 244 - 271Publisher: Cambridge University PressPrint publication year: 2013