Book contents
- Frontmatter
- Contents
- List of Contributors
- Preface
- 1 An Introduction to Next-Generation Biological Platforms
- 2 An Introduction to The Cancer Genome Atlas
- 3 DNA Variant Calling in Targeted Sequencing Data
- 4 Statistical Analysis of Mapped Reads from mRNA-Seq Data
- 5 Model-Based Methods for Transcript Expression-Level Quantification in RNA-Seq
- 6 Bayesian Model-Based Approaches for Solexa Sequencing Data
- 7 Statistical Aspects of ChIP-Seq Analysis
- 8 Bayesian Modeling of ChIP-Seq Data from Transcription Factor to Nucleosome Positioning
- 9 Multivariate Linear Models for GWAS
- 10 Bayesian Model Averaging for Genetic Association Studies
- 11 Whole-Genome Multi-SNP-Phenotype Association Analysis
- 12 Methods for the Analysis of Copy Number Data in Cancer Research
- 13 Bayesian Models for Integrative Genomics
- 14 Bayesian Graphical Models for Integrating Multiplatform Genomics Data
- 15 Genetical Genomics Data: Some Statistical Problems and Solutions
- 16 A Bayesian Framework for Integrating Copy Number and Gene Expression Data
- 17 Application of Bayesian Sparse Factor Analysis Models in Bioinformatics
- 18 Predicting Cancer Subtypes Using Survival-Supervised Latent Dirichlet Allocation Models
- 19 Regularization Techniques for Highly Correlated Gene Expression Data with Unknown Group Structure
- 20 Optimized Cross-Study Analysis of Microarray-Based Predictors
- 21 Functional Enrichment Testing: A Survey of Statistical Methods
- 22 Discover Trend and Progression Underlying High-Dimensional Data
- 23 Bayesian Phylogenetics Adapts to Comprehensive Infectious Disease Sequence Data
- Index
- Plate section
17 - Application of Bayesian Sparse Factor Analysis Models in Bioinformatics
Published online by Cambridge University Press: 05 June 2013
- Frontmatter
- Contents
- List of Contributors
- Preface
- 1 An Introduction to Next-Generation Biological Platforms
- 2 An Introduction to The Cancer Genome Atlas
- 3 DNA Variant Calling in Targeted Sequencing Data
- 4 Statistical Analysis of Mapped Reads from mRNA-Seq Data
- 5 Model-Based Methods for Transcript Expression-Level Quantification in RNA-Seq
- 6 Bayesian Model-Based Approaches for Solexa Sequencing Data
- 7 Statistical Aspects of ChIP-Seq Analysis
- 8 Bayesian Modeling of ChIP-Seq Data from Transcription Factor to Nucleosome Positioning
- 9 Multivariate Linear Models for GWAS
- 10 Bayesian Model Averaging for Genetic Association Studies
- 11 Whole-Genome Multi-SNP-Phenotype Association Analysis
- 12 Methods for the Analysis of Copy Number Data in Cancer Research
- 13 Bayesian Models for Integrative Genomics
- 14 Bayesian Graphical Models for Integrating Multiplatform Genomics Data
- 15 Genetical Genomics Data: Some Statistical Problems and Solutions
- 16 A Bayesian Framework for Integrating Copy Number and Gene Expression Data
- 17 Application of Bayesian Sparse Factor Analysis Models in Bioinformatics
- 18 Predicting Cancer Subtypes Using Survival-Supervised Latent Dirichlet Allocation Models
- 19 Regularization Techniques for Highly Correlated Gene Expression Data with Unknown Group Structure
- 20 Optimized Cross-Study Analysis of Microarray-Based Predictors
- 21 Functional Enrichment Testing: A Survey of Statistical Methods
- 22 Discover Trend and Progression Underlying High-Dimensional Data
- 23 Bayesian Phylogenetics Adapts to Comprehensive Infectious Disease Sequence Data
- Index
- Plate section
Summary
Introduction
Factor analysis is a statistical method for explaining the variation of a large number of observed, correlated variables using a smaller number of unobserved, latent variables (factors). It is closely related to principal component analysis (PCA) and represents one of the most well-known latent variable models. Although PCA determines the components using standard geometrical procedures and is mostly exploratory, factor analysis can be either exploratory or confirmatory. Exploratory factor analysis assumes that any latent factor can be associated with any observed variable (described by the factor loadings) and is suitable for revealing the underlying structure of relatively large data sets. Confirmatory factor analysis is more subjective, where researchers usually have strong prior belief on the number of latent factors as well as the potential variables associated with each factor. Bayesian sparse factor modeling was first introduced by Mike West for the analysis of microarray gene expression profiles (West, 2003) and was later applied to a diverse range of bioinformatics studies including the inference of transcriptional regulatory networks (Sabatti et al., 2005), biological pathway analysis (Carvalho et al., 2008), population structure analysis (Engelhardt and Stephens, 2010), and other areas. In this chapter, we first introduce classical factor analysis models and Bayesian sparse factor analysis models and associated inferential methods. We then review several applications of this general approach in computational biology. This chapter ends with discussion on future developments.
- Type
- Chapter
- Information
- Advances in Statistical BioinformaticsModels and Integrative Inference for High-Throughput Data, pp. 350 - 365Publisher: Cambridge University PressPrint publication year: 2013
- 1
- Cited by