Book contents
- Frontmatter
- Contents
- List of Contributors
- Preface
- 1 An Introduction to High-Throughput Bioinformatics Data
- 2 Hierarchical Mixture Models for Expression Profiles
- 3 Bayesian Hierarchical Models for Inference in Microarray Data
- 4 Bayesian Process-Based Modeling of Two-Channel Microarray Experiments: Estimating Absolute mRNA Concentrations
- 5 Identification of Biomarkers in Classification and Clustering of High-Throughput Data
- 6 Modeling Nonlinear Gene Interactions Using Bayesian MARS
- 7 Models for Probability of Under- and Overexpression: The POE Scale
- 8 Sparse Statistical Modelling in Gene Expression Genomics
- 9 Bayesian Analysis of Cell Cycle Gene Expression Data
- 10 Model-Based Clustering for Expression Data via a Dirichlet Process Mixture Model
- 11 Interval Mapping for Expression Quantitative Trait Loci
- 12 Bayesian Mixture Models for Gene Expression and Protein Profiles
- 13 Shrinkage Estimation for SAGE Data Using a Mixture Dirichlet Prior
- 14 Analysis of Mass Spectrometry Data Using Bayesian Wavelet-Based Functional Mixed Models
- 15 Nonparametric Models for Proteomic Peak Identification and Quantification
- 16 Bayesian Modeling and Inference for Sequence Motif Discovery
- 17 Identification of DNA Regulatory Motifs and Regulators by Integrating Gene Expression and Sequence Data
- 18 A Misclassification Model for Inferring Transcriptional Regulatory Networks
- 19 Estimating Cellular Signaling from Transcription Data
- 20 Computational Methods for Learning Bayesian Networks from High-Throughput Biological Data
- 21 Bayesian Networks and Informative Priors: Transcriptional Regulatory Network Models
- 22 Sample Size Choice for Microarray Experiments
- Plate section
3 - Bayesian Hierarchical Models for Inference in Microarray Data
Published online by Cambridge University Press: 23 November 2009
- Frontmatter
- Contents
- List of Contributors
- Preface
- 1 An Introduction to High-Throughput Bioinformatics Data
- 2 Hierarchical Mixture Models for Expression Profiles
- 3 Bayesian Hierarchical Models for Inference in Microarray Data
- 4 Bayesian Process-Based Modeling of Two-Channel Microarray Experiments: Estimating Absolute mRNA Concentrations
- 5 Identification of Biomarkers in Classification and Clustering of High-Throughput Data
- 6 Modeling Nonlinear Gene Interactions Using Bayesian MARS
- 7 Models for Probability of Under- and Overexpression: The POE Scale
- 8 Sparse Statistical Modelling in Gene Expression Genomics
- 9 Bayesian Analysis of Cell Cycle Gene Expression Data
- 10 Model-Based Clustering for Expression Data via a Dirichlet Process Mixture Model
- 11 Interval Mapping for Expression Quantitative Trait Loci
- 12 Bayesian Mixture Models for Gene Expression and Protein Profiles
- 13 Shrinkage Estimation for SAGE Data Using a Mixture Dirichlet Prior
- 14 Analysis of Mass Spectrometry Data Using Bayesian Wavelet-Based Functional Mixed Models
- 15 Nonparametric Models for Proteomic Peak Identification and Quantification
- 16 Bayesian Modeling and Inference for Sequence Motif Discovery
- 17 Identification of DNA Regulatory Motifs and Regulators by Integrating Gene Expression and Sequence Data
- 18 A Misclassification Model for Inferring Transcriptional Regulatory Networks
- 19 Estimating Cellular Signaling from Transcription Data
- 20 Computational Methods for Learning Bayesian Networks from High-Throughput Biological Data
- 21 Bayesian Networks and Informative Priors: Transcriptional Regulatory Network Models
- 22 Sample Size Choice for Microarray Experiments
- Plate section
Summary
Abstract
We review Bayesian hierarchical models for inference in microarray data. The chapter consists of two main parts that deal with use of Bayesian hierarchical models at different levels of analysis encountered in the context of microarrays. The first part reviews a Bayesian hierarchical model for the estimation of gene expression levels from Affymetrix GeneChip data, and for inference on differential expression. In the second part, an integrated model that incorporates expression-dependent normalization within an ANOVA model of differential expression is reviewed and compared to a model where normalization is preprocessed. The chapter concludes by discussing how predictive Bayesian model checking can be usefully included within the model inference.
Introduction
Background
Microarrays are one of the new technologies that have developed in line with genome sequencing and developments in miniaturization and robotics. The technology exploits the fact that single-stranded RNA (or DNA) molecules have a high affinity to form double-stranded structures. Pairing is specific and complementary strands have particularly high affinity for binding. On microarrays gene-specific sequences are attached in tiny specified locations. By hybridizing a cell sample of fragmented, fluorescently labeled RNA (or DNA) to the array and measuring the fluorescence at the defined locations, one can obtain measures of the amount of the different RNA or DNA transcripts present in the sample hybridized.
Arrays generally contain thousands of spots (or probes) at each of which a particular gene or sequence is represented. In effect, a microarray experiment thus represents data comparable to that obtained by performing tens of thousands of experiments of a similar type in parallel.
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- Chapter
- Information
- Bayesian Inference for Gene Expression and Proteomics , pp. 53 - 74Publisher: Cambridge University PressPrint publication year: 2006
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