Advances in Statistical Bioinformatics: Models and Integrative Inference for High-Throughput Data

doi:10.1017/CBO9781139226448

Providing genome-informed personalized treatment is a goal of modern medicine. Identifying new translational targets in nucleic acid characterizations is an important step toward that goal. The information tsunami produced by such genome-scale investigations is stimulating parallel developments in statistical methodology and inference, analytical frameworks, and computational tools. Within the context of genomic medicine and with a strong focus on cancer research, this book describes the integration of high-throughput bioinformatics data from multiple platforms to inform our understanding of the functional consequences of genomic alterations. This includes rigorous and scalable methods for simultaneously handling diverse data types such as gene expression array, miRNA, copy number, methylation, and next-generation sequencing data. This material is written for statisticians who are interested in modeling and analyzing high-throughput data. Chapters by experts in the field offer a thorough introduction to the biological and technical principles behind multiplatform high-throughput experimentation.

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Contents

Frontmatter
pp i-iv
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Contents
pp v-vi
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List of Contributors
pp vii-x
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Preface
pp xi-xvi
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1 - An Introduction to Next-Generation Biological Platforms
pp 1-30
- By Virginia Mohlere, The University of Texas, Wenting Wang, Translational and Clinical Science, Ganiraju Manyam, The University of Texas
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2 - An Introduction to The Cancer Genome Atlas
pp 31-53
- By Bradley M. Broom, The University of Texas, Rehan Akbani, The University of Texas
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3 - DNA Variant Calling in Targeted Sequencing Data
pp 54-76
- By Wenyi Wang, The University of Texas, Yu Fan, The University of Texas, Terence P. Speed, University of California
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4 - Statistical Analysis of Mapped Reads from mRNA-Seq Data
pp 77-104
- By Ernest Turro, University of Cambridge, Alex Lewin, Imperial College London
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5 - Model-Based Methods for Transcript Expression-Level Quantification in RNA-Seq
pp 105-125
- By Zhaonan Sun, Purdue University, Han Wu, Purdue University, Zhaohui Qin, Emory University, Yu Zhu, Purdue University
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6 - Bayesian Model-Based Approaches for Solexa Sequencing Data
pp 126-137
- By Riten Mitra, University of Texas, Peter Mueller, University of Texas, Yuan Ji, NorthShore University Health-System
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7 - Statistical Aspects of ChIP-Seq Analysis
pp 138-169
- By Jonathan Cairns, University of Cambridge, Andy G. Lynch, University of Cambridge, Simon Tavaré, University of Cambridge
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8 - Bayesian Modeling of ChIP-Seq Data from Transcription Factor to Nucleosome Positioning
pp 170-187
- By Raphael Gottardo, Public Health Sciences Division, Sangsoon Woo, Fred Hutchinson Cancer Research Center
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9 - Multivariate Linear Models for GWAS
pp 188-207
- By Chiara Sabatti, Stanford University
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10 - Bayesian Model Averaging for Genetic Association Studies
pp 208-223
- By Christine Peterson, Rice University, Michael Swartz, The University of Texas, Sanjay Shete, The University of Texas, Marina Vannucci, Rice University
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11 - Whole-Genome Multi-SNP-Phenotype Association Analysis
pp 224-243
- By Yongtao Guan, Baylor College of Medicine, Kai Wang, University of California
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12 - Methods for the Analysis of Copy Number Data in Cancer Research
pp 244-271
- By Bradley M. Broom, The University of Texas, Kim-Anh Do, The University of Texas, Melissa Bondy, Baylor College of Medicine, Patricia Thompson, University of Arizona, Kevin Coombes, The University of Texas
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13 - Bayesian Models for Integrative Genomics
pp 272-291
- By Francesco C. Stingo, The University of Texas, Marina Vannucci, Rice University
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14 - Bayesian Graphical Models for Integrating Multiplatform Genomics Data
pp 292-311
- By Wenting Wang, Translational and Clinical Science, Veerabhadran Baladandayuthapani, The University of Texas, Chris C. Holmes, University of Oxford, Kim-Anh Do, The University of Texas
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15 - Genetical Genomics Data: Some Statistical Problems and Solutions
pp 312-330
- By Hongzhe Li, University of Pennsylvania
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16 - A Bayesian Framework for Integrating Copy Number and Gene Expression Data
pp 331-349
- By Yuan Ji, NorthShore University Health-System, Filippo Trentini, Bocconi University, Peter Mueller, University of Texas
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17 - Application of Bayesian Sparse Factor Analysis Models in Bioinformatics
pp 350-365
- By Haisu Ma, Yale University, Hongyu Zhao, Yale University
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18 - Predicting Cancer Subtypes Using Survival-Supervised Latent Dirichlet Allocation Models
pp 366-381
- By Keegan Korthauer, University of Wisconsin, John Dawson, University of Wisconsin, Christina Kendziorski, University of Wisconsin
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19 - Regularization Techniques for Highly Correlated Gene Expression Data with Unknown Group Structure
pp 382-397
- By Brent A. Johnson, Emory University
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20 - Optimized Cross-Study Analysis of Microarray-Based Predictors
pp 398-422
- By Xiaogang Zhong, Johns Hopkins University, Luigi Marchionni, Johns Hopkins University, Leslie Cope, Johns Hopkins University, Edwin S. Iversen, Duke University, Elizabeth S. Garrett-Mayer, Medical University of South Carolina, Edward Gabrielson, Johns Hopkins University, Giovanni Parmigiani, Harvard University
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21 - Functional Enrichment Testing: A Survey of Statistical Methods
pp 423-444
- By Laila M. Poisson, Department of Public Health Sciences, Debashis Ghosh, Penn State University
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22 - Discover Trend and Progression Underlying High-Dimensional Data
pp 445-459
- By Peng Qiu, The University of Texas
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23 - Bayesian Phylogenetics Adapts to Comprehensive Infectious Disease Sequence Data
pp 460-476
- By Jennifer A. Tom, University of California, Janet S. Sinsheimer, University of California, Marc A. Suchard, University of California
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Index
pp 477-481
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Plate section
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Advances in Statistical Bioinformatics

Models and Integrative Inference for High-Throughput Data

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