Bayesian Analysis of Gene Expression Data / Edition 1

Hardcover (Print)
Used and New from Other Sellers
Used and New from Other Sellers
from $62.00
Usually ships in 1-2 business days
(Save 38%)
Other sellers (Hardcover)
  • All (9) from $62.00   
  • New (5) from $74.55   
  • Used (4) from $62.00   


The field of high-throughput genetic experimentation is evolving rapidly, with the advent of new technologies and new venues for data mining. Bayesian methods play a role central to the future of data and knowledge integration in the field of Bioinformatics. This book is devoted exclusively to Bayesian methods of analysis for applications to high-throughput gene expression data, exploring the relevant methods that are changing Bioinformatics. Case studies, illustrating Bayesian analyses of public gene expression data, provide the backdrop for students to develop analytical skills, while the more experienced readers will find the review of advanced methods challenging and attainable.

This book:

  • Introduces the fundamentals in Bayesian methods of analysis for applications to high-throughput gene expression data.
  • Provides an extensive review of Bayesian analysis and advanced topics for Bioinformatics, including examples that extensively detail the necessary applications.
  • Accompanied by website featuring datasets, exercises and solutions.

Bayesian Analysis of Gene Expression Data offers a unique introduction to both Bayesian analysis and gene expression, aimed at graduate students in Statistics, Biomedical Engineers, Computer Scientists, Biostatisticians, Statistical Geneticists, Computational Biologists, applied Mathematicians and Medical consultants working in genomics. Bioinformatics researchers from many fields will find much value in this book.

Read More Show Less

Editorial Reviews

From the Publisher
“The target audience for this book is clearly statisticians rather than biologists … It does provide a very useful overview of statistical genomics for anyone working in the field.”  (The Quarterly Review of Biology, 1 March 2012)

"Bioinformatics researchers from many fields will find much value in this book." (Mathematical Reviews, 2011)

"Experienced readers will find the review of advanced methods for bioinformatics challenging and attainable. This book will interest graduate students in statistics and bioinformatics researchers from many fields." (Book News, December 2009)

Read More Show Less

Product Details

  • ISBN-13: 9780470517666
  • Publisher: Wiley
  • Publication date: 9/22/2009
  • Series: Statistics in Practice Series, #130
  • Edition description: New Edition
  • Edition number: 1
  • Pages: 252
  • Product dimensions: 6.10 (w) x 9.20 (h) x 0.80 (d)

Meet the Author

Bani Mallick, Department of Statistics, Texas A&M University, USA.

Veera Balandandayuthapani, Department of Biostatistics, Anderson Cancer Center, Texas, USA.

David L. Gold, Department of Biostatistics, School of Public Health and Health Professions, University at Buffalo, The State University of New York, USA.

Read More Show Less

Table of Contents

Table of Notation.

1 Bioinformatics and Gene Expression Experiments.

1.1 Introduction.

1.2 About This Book.

2 Basic Biology.

2.1 Background.

2.1.1 DNA Structures and Transcription.

2.2 Gene Expression Microarray Experiments.

3 Bayesian Linear Models for Gene Expression.

3.1 Introduction.

3.2 Bayesian Analysis of a Linear Model.

3.3 Bayesian Linear Models for Differential Expression.

3.4 Bayesian ANOVA for Gene Selection.

3.5 Robust ANOVA model with Mixtures of Singular Distributions.

3.6 Case Study.

3.7 Accounting for Nuisance Effects.

3.8 Summary and Further Reading.

4 Bayesian Multiple Testing and False Discovery Rate Analysis.

4.1 Introduction to Multiple Testing.

4.2 False Discovery Rate Analysis.

4.3 Bayesian False Discovery Rate Analysis.

4.4 Bayesian Estimation of FDR.

4.5 FDR and Decision Theory.

4.6 FDR and bFDR Summary.

5 Bayesian Classification for Microarray Data.

5.1 Introduction.

5.2 Classification and Discriminant Rules.

5.3 Bayesian Discriminant Analysis.

5.4 Bayesian Regression Based Approaches to Classification.

5.5 Bayesian Nonlinear Classification.

5.6 Prediction and Model Choice.

5.7 Examples.

5.8 Discussion.

6 Bayesian Hypothesis Inference for Gene Classes.

6.1 Interpreting Microarray Results.

6.2 Gene Classes.

6.3 Bayesian Enrichment Analysis.

6.4 Multivariate Gene Class Detection.

6.5 Summary.

7 Unsupervised Classification and Bayesian Clustering.

7.1 Introduction to Bayesian Clustering for Gene Expression Data.

7.2 Hierarchical Clustering.

7.3 K-Means Clustering.

7.4 Model-Based Clustering.

7.5 Model-Based Agglomerative Hierarchical Clustering.

7.6 Bayesian Clustering.

7.7 Principal Components.

7.8 Mixture Modeling.

7.8.1 Label Switching.

7.9 Clustering Using Dirichlet Process Prior.

7.9.1 Infinite Mixture of Gaussian Distributions.

8 Bayesian Graphical Models.

8.1 Introduction.

8.2 Probabilistic Graphical Models.

8.3 Bayesian Networks.

8.4 Inference for Network Models.

9 Advanced Topics.

9.1 Introduction.

9.2 Analysis of Time Course Gene Expression Data.

9.3 Survival Prediction Using Gene Expression Data.

Appendix A: Basics of Bayesian Modeling.

A.1 Basics.

A.1.1 The General Representation Theorem.

A.1.2 Bayes’ Theorem.

A.1.3 Models Based on Partial Exchangeability.

A.1.4 Modeling with Predictors.

A.1.5 Prior Distributions.

A.1.6 Decision Theory and Posterior and Predictive Inferences.

A.1.7 Predictive Distributions.

A.1.8 Examples.

A.2 Bayesian Model Choice.

A.3 Hierarchical Modeling.

A.4 Bayesian Mixture Modeling.

A.5 Bayesian Model Averaging.

Appendix B: Bayesian Computation Tools.

B.1 Overview.

B.2 Large-Sample Posterior Approximations.

B.2.1 The Bayesian Central Limit Theorem.

B.2.2 Laplace’s Method.

B.3 Monte Carlo Integration.

B.4 Importance Sampling.

B.5 Rejection Sampling.

B.6 Gibbs Sampling.

B.7 The Metropolis Algorithm and Metropolis–Hastings.

B.8 Advanced Computational Methods.

B.8.1 Block MCMC.

B.8.2 Truncated Posterior Spaces.

B.8.3 Latent Variables and the Auto-Probit Model.

B.8.4 Bayesian Simultaneous Credible Envelopes.

B.8.5 Proposal Updating.

B.9 Posterior Convergence Diagnostics.

B.10 MCMC Convergence and the Proposal.

B.10.1 Graphical Checks for MCMC Methods.

B.10.2 Convergence Statistics.

B.10.3 MCMC in High-Throughput Analysis.

B.11 Summary.



Read More Show Less

Customer Reviews

Be the first to write a review
( 0 )
Rating Distribution

5 Star


4 Star


3 Star


2 Star


1 Star


Your Rating:

Your Name: Create a Pen Name or

Barnes & Review Rules

Our reader reviews allow you to share your comments on titles you liked, or didn't, with others. By submitting an online review, you are representing to Barnes & that all information contained in your review is original and accurate in all respects, and that the submission of such content by you and the posting of such content by Barnes & does not and will not violate the rights of any third party. Please follow the rules below to help ensure that your review can be posted.

Reviews by Our Customers Under the Age of 13

We highly value and respect everyone's opinion concerning the titles we offer. However, we cannot allow persons under the age of 13 to have accounts at or to post customer reviews. Please see our Terms of Use for more details.

What to exclude from your review:

Please do not write about reviews, commentary, or information posted on the product page. If you see any errors in the information on the product page, please send us an email.

Reviews should not contain any of the following:

  • - HTML tags, profanity, obscenities, vulgarities, or comments that defame anyone
  • - Time-sensitive information such as tour dates, signings, lectures, etc.
  • - Single-word reviews. Other people will read your review to discover why you liked or didn't like the title. Be descriptive.
  • - Comments focusing on the author or that may ruin the ending for others
  • - Phone numbers, addresses, URLs
  • - Pricing and availability information or alternative ordering information
  • - Advertisements or commercial solicitation


  • - By submitting a review, you grant to Barnes & and its sublicensees the royalty-free, perpetual, irrevocable right and license to use the review in accordance with the Barnes & Terms of Use.
  • - Barnes & reserves the right not to post any review -- particularly those that do not follow the terms and conditions of these Rules. Barnes & also reserves the right to remove any review at any time without notice.
  • - See Terms of Use for other conditions and disclaimers.
Search for Products You'd Like to Recommend

Recommend other products that relate to your review. Just search for them below and share!

Create a Pen Name

Your Pen Name is your unique identity on It will appear on the reviews you write and other website activities. Your Pen Name cannot be edited, changed or deleted once submitted.

Your Pen Name can be any combination of alphanumeric characters (plus - and _), and must be at least two characters long.

Continue Anonymously

    If you find inappropriate content, please report it to Barnes & Noble
    Why is this product inappropriate?
    Comments (optional)