Description
CAMBRIDGE Advances in Statistical Bioinformatics Models and Integrative Inference for High-Throughput Data 2014 Edition by Kim-Anh Do, Zhaohui Steve Qin, Marina Vannucci
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. Table of contents :- 1. An introduction to next-generation biological platforms Virginia Mohlere, Wenting Wang and Ganiraju Manyam; 2. An introduction to the cancer genome atlas Bradley M. Broom and Rehan Akbani; 3. DNA variant calling in targeted sequencing data Wenyi Wang, Yu Fan and Terence P. Speed; 4. Statistical analysis of mapped reads from mRNA-seq data Ernest Turro and Alex Lewin; 5. Model-based methods for transcript expression level quantification in RNA-seq Zhaonan Sun, Han Wu and Yu Zhu; 6. Bayesian model-based approaches for solexa sequencing data Riten Mitra, Peter Mueller and Yuan Ji; 7. Statistical aspects of ChIP-seq analysis Jonathan Cairns, Andy G. Lynch and Simon Tavare; 8. Bayesian modeling of ChIP-seq data from transcription factor to nucleosome positioning Raphael Gottardo and Sangsoon Woo; 9. Multivariate linear models for GWAS Chiara Sabatti; 10. Bayesian model averaging for genetic association studies Christine Peterson, Michael Swartz, Sanjay Shete and Marina Vannucci; 11. Whole-genome multi-SNP-phenotype association analysis Yongtao Guan and Kai Wang; 12. Methods for the analysis of copy number data in cancer research Bradley M. Broom, Kim-Anh Do, Melissa Bondy, Patricia Thompson and Kevin Coombes; 13. Bayesian models for integrative genomics Francesco C. Stingo and Marina Vannucci; 14. Bayesian graphical models for integrating multiplatform genomics data Wenting Wang, Veerabhadran Baladandayuthapani, Chris C. Holmes and Kim-Anh Do; 15. Genetical genomics data: some statistical problems and solutions Hongzhe Li; 16. A Bayesian framework for integrating copy number and gene expression data Yuan Ji, Filippo Trentini and Peter Muller; 17. Application of Bayesian sparse factor analysis models in bioinformatics Haisu Ma and Hongyu Zhao; 18. Predicting cancer subtypes using survival-supervised latent Dirichlet allocation models Keegan Korthauer, John Dawson and Christina Kendziorski; 19. Regularization techniques for highly correlated gene expression data with unknown group structure Brent A. Johnson; 20. Optimized cross-study analysis of microarray-based predictors Xiaogang Zhong, Luigi Marchionni, Leslie Cope, Edwin S. Iversen, Elizabeth S. Garrett-Mayer, Edward Gabrielson and Giovanni Parmigiani; 21. Functional enrichment testing: a survey of statistical methods Laila M. Poisson; 22. Discover trend and progression underlying high-dimensional data Peng Qiu; 23. Bayesian phylogenetics adapts to comprehensive infectious disease sequence data Jennifer A. Tom, Janet S. Sinsheimer and Marc A. Suchard.