Bayesian Inference for Gene Expression and Proteomics

Expert overviews of Bayesian methodology, tools and software for multi-platform high-throughput experimentation.

Kim-Anh Do (Edited by), Peter Müller (Edited by), Marina Vannucci (Edited by)

9781107636989, Cambridge University Press

Paperback / softback, published 30 April 2012

456 pages, 22 tables
22.1 x 14.1 x 2.5 cm, 0.57 kg

'Overall, I find this text an excellent contribution to the literature on statistical methods for high throughput genomic and proteomic data analysis. The chapters are well written, the case studies are informative, and the range of topics covered is quite broad and generally logically grouped. I would highly recommend this text to both those people already working in the area and those wanting to break in. It is not only suitable for researchers developing their own methodologies but also for applied quantitative scientists looking for the most cutting-edge tools to analyze their high throughput datasets.' J. Sunil Rao, Biometrics

The interdisciplinary nature of bioinformatics presents a research challenge in integrating concepts, methods, software and multiplatform data. Although there have been rapid developments in new technology and an inundation of statistical methods for addressing other types of high-throughput data, such as proteomic profiles that arise from mass spectrometry experiments. This book discusses the development and application of Bayesian methods in the analysis of high-throughput bioinformatics data that arise from medical, in particular, cancer research, as well as molecular and structural biology. The Bayesian approach has the advantage that evidence can be easily and flexibly incorporated into statistical methods. A basic overview of the biological and technical principles behind multi-platform high-throughput experimentation is followed by expert reviews of Bayesian methodology, tools and software for single group inference, group comparisons, classification and clustering, motif discovery and regulatory networks, and Bayesian networks and gene interactions.

1. An introduction to high-throughput bioinformatics data Keith Baggerly, Kevin Coombes and Jeffrey S. Morris
2. Hierarchical mixture models for expression profiles Michael Newton, Ping Wang and Christina Kendziorski
3. Bayesian hierarchical models for inference in microarray data Anne-Mette K. Hein, Alex Lewin and Sylvia Richardson
4. Bayesian process-based modeling of two-channel microarray experiments estimating absolute mRNA concentrations Mark A. van de Wiel, Marit Holden, Ingrid K. Glad, Heidi Lyng and Arnoldo Frigessi
5. Identification of biomarkers in classification and clustering of high-throughput data Mahlet Tadesse, Marina Vannucci, Naijun Sha and Sinae Kim
6. Modeling nonlinear gene interactions using Bayesian MARS Veerabhadran Baladandayuthapani, Chris C. Holmes, Bani K. Mallick and Raymond J. Carroll
7. Models for probability of under- and over-expression: the POE scale Elizabeth Garrett-Mayer and Robert Scharpf
8. Sparse statistical modelling in gene expression genomics Joseph Lucas, Carlos Carvalho, Quanli Wang, Andrea Bild, Joseph Nevins and Mike West
9. Bayesian analysis of cell-cycle gene expression Chuan Zhou, Jon Wakefield and Linda L. Breeden
10. Model-based clustering for expression data via a Dirichlet process mixture model David Dahl
11. Interval mapping for Expression Quantitative Trait Loci mapping Meng Chen and Christina Kendziorski
12. Bayesian mixture model for gene expression and protein profiles Michele Guindani, Kim-Anh Do, Peter Müller and Jeffrey S. Morris
13. Shrinkage estimation for SAGE data using a mixture Dirichlet prior Jeffrey S. Morris, Kevin Coombes and Keith Baggerly
14. Analysis of mass spectrometry data using Bayesian wavelet-based functional mixed models Jeffrey S. Morris, Philip J. Brown, Keith Baggerly and Kevin Coombes
15. Nonparametric models for proteomic peak identification and quantification Merlise Clyde, Leanna House and Robert Wolpert
16. Bayesian modeling and inference for sequence motif discovery Mayetri Gupta and Jun S. Liu
17. Identifying of DNA regulatory motifs and regulators by integrating gene expression and sequence data Deuk Woo Kwon, Sinae Kim, David Dahl, Michael Swartz, Mahlet Tadesse and Marina Vannucci
18. A misclassification model for inferring transcriptional regulatory networks Ning Sun and Hongyu Zhao
19. Estimating cellular signaling from transcription data Andrew V. Kossenkov, Ghislain Bidaut and Michael Ochs
20. Computational methods for learning Bayesian networks from high-throughput biological data Bradley Broom and Devika Subramanian
21. Modeling transcriptional regulation: Bayesian networks and informative priors Alexander J. Hartemink
22. Sample size choice for microarray experiments Peter Müller, Christian Robert and Judith Rousseau.

Subject Areas: Probability & statistics [PBT], Epidemiology & medical statistics [MBNS]