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Analyzing microarray gene expression data [[electronic resource] /] / Geoffrey J. McLachlan, Kim-Anh Do, Christopher Ambroise
| Analyzing microarray gene expression data [[electronic resource] /] / Geoffrey J. McLachlan, Kim-Anh Do, Christopher Ambroise |
| Autore | McLachlan Geoffrey J. <1946-> |
| Pubbl/distr/stampa | Hoboken, N.J., : Wiley-Interscience, c2004 |
| Descrizione fisica | 1 online resource (366 p.) |
| Disciplina |
572.8636
572.865 |
| Altri autori (Persone) |
DoKim-Anh <1960->
AmbroiseChristophe <1969-> |
| Collana | Wiley series in probability and statistics |
| Soggetto topico |
DNA microarrays - Statistical methods
Gene expression - Statistical methods |
| Soggetto genere / forma | Electronic books. |
| ISBN |
1-280-25332-0
9786610253326 0-470-35030-X 0-471-72612-5 0-471-72842-X |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Analyzing Microarray Gene Expression Data; Contents; Preface; 1 Microarrays in Gene Expression Studies; 1.1 Introduction; 1.2 Background Biology; 1.2.1 Genome, Genotype, and Gene Expression; 1.2.2 Of Wild-Types and Other Alleles; 1.2.3 Aspects of Underlying Biology and Physiochemistry; 1.3 Polymerase Chain Reaction; 1.4 cDNA; 1.4.1 Expressed Sequence Tag; 1.5 Microarray Technology and Application; 1.5.1 History of Microarray Development; 1.5.2 Tools of Microarray Technology; 1.5.3 Limitations of Microarray Technology; 1.5.4 Oligonucleotides versus cDNA Arrays
1.5.5 SAGE: Another Method for Detecting and Measuring Gene Expression Levels1.5.6 Emerging Technologies; 1.6 Sampling of Relevant Research Entities and Public Resources; 2 Cleaning and Normalization; 2.1 Introduction; 2.2 Cleaning Procedures; 2.2.1 Image Processing to Extract Information; 2.2.2 Missing Value Estimation; 2.2.3 Sources of Nonlinearity; 2.3 Normalization and Plotting Procedures for Oligonucleotide Arrays; 2.3.1 Global Approaches for Oligonucleotide Array Data; 2.3.2 Spiked Standard Approaches; 2.3.3 Geometric Mean and Linear Regression Normalization for Multiple Arrays 2.3.4 Nonlinear Normalization for Multiple Arrays Using Smooth Curves2.4 Normalization Methods for cDNA Microarray Data; 2.4.1 Single-Array Normalization; 2.4.2 Multiple Slides Normalization; 2.4.3 ANOVA and Related Methods for Normalization; 2.4.4 Mixed-Model Method for Normalization; 2.4.5 SNOMAD; 2.5 Transformations and Replication; 2.5.1 Importance of Replication; 2.5.2 Transformations; 2.6 Analysis of the Alon Data Set; 2.7 Comparison of Normalization Strategies and Discussion; 3 Some Cluster Analysis Methods; 3.1 Introduction; 3.2 Reduction in the Dimension of the Feature Space 3.3 Cluster Analysis3.4 Some Hierarchical Agglomerative Techniques; 3.5 k-Means Clustering; 3.6 Cluster Analysis with No A Priori Metric; 3.7 Clustering via Finite Mixture Models; 3.7.1 Definition; 3.7.2 Advantages of Model-Based Clustering; 3.8 Fitting Mixture Models Via the EM Algorithm; 3.8.1 E-Step; 3.8.2 M-Step; 3.8.3 Choice of Starting Values for the EM Algorithm; 3.9 Clustering Via Normal Mixtures; 3.9.1 Heteroscedastic Components; 3.9.2 Homoscedastic Components; 3.9.3 Spherical Components; 3.9.4 Choice of Root; 3.9.5 Available Software; 3.10 Mixtures of t Distributions 3.11 Mixtures of Factor Analyzers3.12 Choice of Clustering Solution; 3.13 Classification ML Approach; 3.14 Mixture Models for Clinical and Microarray Data; 3.14.1 Unconditional Approach; 3.14.2 Conditional Approach; 3.15 Choice of the Number of Components in a Mixture Model; 3.15.1 Order of a Mixture Model; 3.15.2 Approaches for Assessing Mixture Order; 3.15.3 Bayesian Information Criterion; 3.15.4 Integrated Classification Likelihood Criterion; 3.16 Resampling Approach; 3.17 Other Resampling Approaches for Number of Clusters; 3.17.1 The Gap Statistic 3.17.2 The Clest Method for the Number of Clusters |
| Record Nr. | UNINA-9910146082203321 |
McLachlan Geoffrey J. <1946->
|
||
| Hoboken, N.J., : Wiley-Interscience, c2004 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Analyzing microarray gene expression data [[electronic resource] /] / Geoffrey J. McLachlan, Kim-Anh Do, Christopher Ambroise
| Analyzing microarray gene expression data [[electronic resource] /] / Geoffrey J. McLachlan, Kim-Anh Do, Christopher Ambroise |
| Autore | McLachlan Geoffrey J. <1946-> |
| Pubbl/distr/stampa | Hoboken, N.J., : Wiley-Interscience, c2004 |
| Descrizione fisica | 1 online resource (366 p.) |
| Disciplina |
572.8636
572.865 |
| Altri autori (Persone) |
DoKim-Anh <1960->
AmbroiseChristophe <1969-> |
| Collana | Wiley series in probability and statistics |
| Soggetto topico |
DNA microarrays - Statistical methods
Gene expression - Statistical methods |
| ISBN |
1-280-25332-0
9786610253326 0-470-35030-X 0-471-72612-5 0-471-72842-X |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Analyzing Microarray Gene Expression Data; Contents; Preface; 1 Microarrays in Gene Expression Studies; 1.1 Introduction; 1.2 Background Biology; 1.2.1 Genome, Genotype, and Gene Expression; 1.2.2 Of Wild-Types and Other Alleles; 1.2.3 Aspects of Underlying Biology and Physiochemistry; 1.3 Polymerase Chain Reaction; 1.4 cDNA; 1.4.1 Expressed Sequence Tag; 1.5 Microarray Technology and Application; 1.5.1 History of Microarray Development; 1.5.2 Tools of Microarray Technology; 1.5.3 Limitations of Microarray Technology; 1.5.4 Oligonucleotides versus cDNA Arrays
1.5.5 SAGE: Another Method for Detecting and Measuring Gene Expression Levels1.5.6 Emerging Technologies; 1.6 Sampling of Relevant Research Entities and Public Resources; 2 Cleaning and Normalization; 2.1 Introduction; 2.2 Cleaning Procedures; 2.2.1 Image Processing to Extract Information; 2.2.2 Missing Value Estimation; 2.2.3 Sources of Nonlinearity; 2.3 Normalization and Plotting Procedures for Oligonucleotide Arrays; 2.3.1 Global Approaches for Oligonucleotide Array Data; 2.3.2 Spiked Standard Approaches; 2.3.3 Geometric Mean and Linear Regression Normalization for Multiple Arrays 2.3.4 Nonlinear Normalization for Multiple Arrays Using Smooth Curves2.4 Normalization Methods for cDNA Microarray Data; 2.4.1 Single-Array Normalization; 2.4.2 Multiple Slides Normalization; 2.4.3 ANOVA and Related Methods for Normalization; 2.4.4 Mixed-Model Method for Normalization; 2.4.5 SNOMAD; 2.5 Transformations and Replication; 2.5.1 Importance of Replication; 2.5.2 Transformations; 2.6 Analysis of the Alon Data Set; 2.7 Comparison of Normalization Strategies and Discussion; 3 Some Cluster Analysis Methods; 3.1 Introduction; 3.2 Reduction in the Dimension of the Feature Space 3.3 Cluster Analysis3.4 Some Hierarchical Agglomerative Techniques; 3.5 k-Means Clustering; 3.6 Cluster Analysis with No A Priori Metric; 3.7 Clustering via Finite Mixture Models; 3.7.1 Definition; 3.7.2 Advantages of Model-Based Clustering; 3.8 Fitting Mixture Models Via the EM Algorithm; 3.8.1 E-Step; 3.8.2 M-Step; 3.8.3 Choice of Starting Values for the EM Algorithm; 3.9 Clustering Via Normal Mixtures; 3.9.1 Heteroscedastic Components; 3.9.2 Homoscedastic Components; 3.9.3 Spherical Components; 3.9.4 Choice of Root; 3.9.5 Available Software; 3.10 Mixtures of t Distributions 3.11 Mixtures of Factor Analyzers3.12 Choice of Clustering Solution; 3.13 Classification ML Approach; 3.14 Mixture Models for Clinical and Microarray Data; 3.14.1 Unconditional Approach; 3.14.2 Conditional Approach; 3.15 Choice of the Number of Components in a Mixture Model; 3.15.1 Order of a Mixture Model; 3.15.2 Approaches for Assessing Mixture Order; 3.15.3 Bayesian Information Criterion; 3.15.4 Integrated Classification Likelihood Criterion; 3.16 Resampling Approach; 3.17 Other Resampling Approaches for Number of Clusters; 3.17.1 The Gap Statistic 3.17.2 The Clest Method for the Number of Clusters |
| Record Nr. | UNINA-9910830637803321 |
|
McLachlan Geoffrey J. <1946->
|
||
| Hoboken, N.J., : Wiley-Interscience, c2004 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Analyzing microarray gene expression data / / Geoffrey J. McLachlan, Kim-Anh Do, Christopher Ambroise
| Analyzing microarray gene expression data / / Geoffrey J. McLachlan, Kim-Anh Do, Christopher Ambroise |
| Autore | McLachlan Geoffrey J. <1946-> |
| Pubbl/distr/stampa | Hoboken, N.J., : Wiley-Interscience, c2004 |
| Descrizione fisica | 1 online resource (366 p.) |
| Disciplina | 572.8/636 |
| Altri autori (Persone) |
DoKim-Anh <1960->
AmbroiseChristophe <1969-> |
| Collana | Wiley series in probability and statistics |
| Soggetto topico |
DNA microarrays - Statistical methods
Gene expression - Statistical methods |
| ISBN |
1-280-25332-0
9786610253326 0-470-35030-X 0-471-72612-5 0-471-72842-X |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Analyzing Microarray Gene Expression Data; Contents; Preface; 1 Microarrays in Gene Expression Studies; 1.1 Introduction; 1.2 Background Biology; 1.2.1 Genome, Genotype, and Gene Expression; 1.2.2 Of Wild-Types and Other Alleles; 1.2.3 Aspects of Underlying Biology and Physiochemistry; 1.3 Polymerase Chain Reaction; 1.4 cDNA; 1.4.1 Expressed Sequence Tag; 1.5 Microarray Technology and Application; 1.5.1 History of Microarray Development; 1.5.2 Tools of Microarray Technology; 1.5.3 Limitations of Microarray Technology; 1.5.4 Oligonucleotides versus cDNA Arrays
1.5.5 SAGE: Another Method for Detecting and Measuring Gene Expression Levels1.5.6 Emerging Technologies; 1.6 Sampling of Relevant Research Entities and Public Resources; 2 Cleaning and Normalization; 2.1 Introduction; 2.2 Cleaning Procedures; 2.2.1 Image Processing to Extract Information; 2.2.2 Missing Value Estimation; 2.2.3 Sources of Nonlinearity; 2.3 Normalization and Plotting Procedures for Oligonucleotide Arrays; 2.3.1 Global Approaches for Oligonucleotide Array Data; 2.3.2 Spiked Standard Approaches; 2.3.3 Geometric Mean and Linear Regression Normalization for Multiple Arrays 2.3.4 Nonlinear Normalization for Multiple Arrays Using Smooth Curves2.4 Normalization Methods for cDNA Microarray Data; 2.4.1 Single-Array Normalization; 2.4.2 Multiple Slides Normalization; 2.4.3 ANOVA and Related Methods for Normalization; 2.4.4 Mixed-Model Method for Normalization; 2.4.5 SNOMAD; 2.5 Transformations and Replication; 2.5.1 Importance of Replication; 2.5.2 Transformations; 2.6 Analysis of the Alon Data Set; 2.7 Comparison of Normalization Strategies and Discussion; 3 Some Cluster Analysis Methods; 3.1 Introduction; 3.2 Reduction in the Dimension of the Feature Space 3.3 Cluster Analysis3.4 Some Hierarchical Agglomerative Techniques; 3.5 k-Means Clustering; 3.6 Cluster Analysis with No A Priori Metric; 3.7 Clustering via Finite Mixture Models; 3.7.1 Definition; 3.7.2 Advantages of Model-Based Clustering; 3.8 Fitting Mixture Models Via the EM Algorithm; 3.8.1 E-Step; 3.8.2 M-Step; 3.8.3 Choice of Starting Values for the EM Algorithm; 3.9 Clustering Via Normal Mixtures; 3.9.1 Heteroscedastic Components; 3.9.2 Homoscedastic Components; 3.9.3 Spherical Components; 3.9.4 Choice of Root; 3.9.5 Available Software; 3.10 Mixtures of t Distributions 3.11 Mixtures of Factor Analyzers3.12 Choice of Clustering Solution; 3.13 Classification ML Approach; 3.14 Mixture Models for Clinical and Microarray Data; 3.14.1 Unconditional Approach; 3.14.2 Conditional Approach; 3.15 Choice of the Number of Components in a Mixture Model; 3.15.1 Order of a Mixture Model; 3.15.2 Approaches for Assessing Mixture Order; 3.15.3 Bayesian Information Criterion; 3.15.4 Integrated Classification Likelihood Criterion; 3.16 Resampling Approach; 3.17 Other Resampling Approaches for Number of Clusters; 3.17.1 The Gap Statistic 3.17.2 The Clest Method for the Number of Clusters |
| Record Nr. | UNINA-9910877596703321 |
|
McLachlan Geoffrey J. <1946->
|
||
| Hoboken, N.J., : Wiley-Interscience, c2004 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Bayesian analysis of gene expression data [[electronic resource] /] / edited by Bani Mallick, David Gold, and Veera Baladandayuthapani
| Bayesian analysis of gene expression data [[electronic resource] /] / edited by Bani Mallick, David Gold, and Veera Baladandayuthapani |
| Autore | Mallick Bani K. <1965-> |
| Pubbl/distr/stampa | Hoboken, N.J., : Wiley, 2009 |
| Descrizione fisica | 1 online resource (254 p.) |
| Disciplina |
572.8
572.86501519542 |
| Altri autori (Persone) |
MallickBani K. <1965->
GoldDavid <1970-> BaladandayuthapaniVeerabhadran <1976-> |
| Collana | Statistics in practice |
| Soggetto topico |
Gene expression - Statistical methods
Bayesian statistical decision theory |
| ISBN |
1-282-34942-2
9786612349423 0-470-74278-X 0-470-74281-X |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Bayesian Analysis of Gene Expression Data; Contents; Table of Notation; 1 Bioinformatics and Gene Expression Experiments; 1.1 Introduction; 1.2 About This Book; 2 Gene Expression Data: Basic Biology and Experiments; 2.1 Background Biology; 2.1.1 DNA Structures and Transcription; 2.2 Gene Expression Microarray Experiments; 2.2.1 Microarray Designs; 2.2.2 Work Flow; 2.2.3 Data Cleaning; 3 Bayesian Linear Models for Gene Expression; 3.1 Introduction; 3.2 Bayesian Analysis of a Linear Model; 3.2.1 Analysis via Conjugate Priors; 3.2.2 Bayesian Variable Selection; 3.2.3 Model Selection Priors
3.2.4 Priors on Regression Coefficients3.2.5 Sparsity Priors; 3.3 Bayesian Linear Models for Differential Expression; 3.3.1 Relevant Work; 3.4 Bayesian ANOVA for Gene Selection; 3.4.1 The Basic Bayesian ANOVA Model; 3.4.2 Differential Expression via Model 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; 4.2.1 Theoretical Developments 4.2.2 FDR Analysis with Gene Expression Arrays4.3 Bayesian False Discovery Rate Analysis; 4.3.1 Theoretical Developments; 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; 5.4.1 Bayesian Analysis of Generalized Linear Models; 5.4.2 Link Functions; 5.4.3 GLM using Latent Processes; 5.4.4 Priors and Computation 5.4.5 Bayesian Probit Regression using Auxiliary Variables5.5 Bayesian Nonlinear Classification; 5.5.1 Classification using Interactions; 5.5.2 Classification using Kernel Methods; 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; 6.2.1 Enrichment Analysis; 6.3 Bayesian Enrichment Analysis; 6.4 Multivariate Gene Class Detection; 6.4.1 Extending the Bayesian ANOVA Model; 6.4.2 Bayesian Decomposition; 6.5 Summary; 7 Unsupervised Classification and Bayesian Clustering 7.1 Introduction to Bayesian Clustering for Gene Expression Data7.2 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; 8.4.1 Multinomial-Dirichlet Model; 8.4.2 Gaussian Model 8.4.3 Model Search |
| Record Nr. | UNINA-9910139785403321 |
|
Mallick Bani K. <1965->
|
||
| Hoboken, N.J., : Wiley, 2009 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Bayesian analysis of gene expression data [[electronic resource] /] / edited by Bani Mallick, David Gold, and Veera Baladandayuthapani
| Bayesian analysis of gene expression data [[electronic resource] /] / edited by Bani Mallick, David Gold, and Veera Baladandayuthapani |
| Autore | Mallick Bani K. <1965-> |
| Pubbl/distr/stampa | Hoboken, N.J., : Wiley, 2009 |
| Descrizione fisica | 1 online resource (254 p.) |
| Disciplina |
572.8
572.86501519542 |
| Altri autori (Persone) |
MallickBani K. <1965->
GoldDavid <1970-> BaladandayuthapaniVeerabhadran <1976-> |
| Collana | Statistics in practice |
| Soggetto topico |
Gene expression - Statistical methods
Bayesian statistical decision theory |
| ISBN |
1-282-34942-2
9786612349423 0-470-74278-X 0-470-74281-X |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Bayesian Analysis of Gene Expression Data; Contents; Table of Notation; 1 Bioinformatics and Gene Expression Experiments; 1.1 Introduction; 1.2 About This Book; 2 Gene Expression Data: Basic Biology and Experiments; 2.1 Background Biology; 2.1.1 DNA Structures and Transcription; 2.2 Gene Expression Microarray Experiments; 2.2.1 Microarray Designs; 2.2.2 Work Flow; 2.2.3 Data Cleaning; 3 Bayesian Linear Models for Gene Expression; 3.1 Introduction; 3.2 Bayesian Analysis of a Linear Model; 3.2.1 Analysis via Conjugate Priors; 3.2.2 Bayesian Variable Selection; 3.2.3 Model Selection Priors
3.2.4 Priors on Regression Coefficients3.2.5 Sparsity Priors; 3.3 Bayesian Linear Models for Differential Expression; 3.3.1 Relevant Work; 3.4 Bayesian ANOVA for Gene Selection; 3.4.1 The Basic Bayesian ANOVA Model; 3.4.2 Differential Expression via Model 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; 4.2.1 Theoretical Developments 4.2.2 FDR Analysis with Gene Expression Arrays4.3 Bayesian False Discovery Rate Analysis; 4.3.1 Theoretical Developments; 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; 5.4.1 Bayesian Analysis of Generalized Linear Models; 5.4.2 Link Functions; 5.4.3 GLM using Latent Processes; 5.4.4 Priors and Computation 5.4.5 Bayesian Probit Regression using Auxiliary Variables5.5 Bayesian Nonlinear Classification; 5.5.1 Classification using Interactions; 5.5.2 Classification using Kernel Methods; 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; 6.2.1 Enrichment Analysis; 6.3 Bayesian Enrichment Analysis; 6.4 Multivariate Gene Class Detection; 6.4.1 Extending the Bayesian ANOVA Model; 6.4.2 Bayesian Decomposition; 6.5 Summary; 7 Unsupervised Classification and Bayesian Clustering 7.1 Introduction to Bayesian Clustering for Gene Expression Data7.2 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; 8.4.1 Multinomial-Dirichlet Model; 8.4.2 Gaussian Model 8.4.3 Model Search |
| Record Nr. | UNINA-9910830664603321 |
|
Mallick Bani K. <1965->
|
||
| Hoboken, N.J., : Wiley, 2009 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Bayesian analysis of gene expression data / / edited by Bani Mallick, David Gold, and Veera Baladandayuthapani
| Bayesian analysis of gene expression data / / edited by Bani Mallick, David Gold, and Veera Baladandayuthapani |
| Pubbl/distr/stampa | Hoboken, N.J., : Wiley, 2009 |
| Descrizione fisica | 1 online resource (254 p.) |
| Disciplina |
572.8
572.86501519542 |
| Altri autori (Persone) |
MallickBani K. <1965->
GoldDavid <1970-> BaladandayuthapaniVeerabhadran <1976-> |
| Collana | Statistics in practice |
| Soggetto topico |
Gene expression - Statistical methods
Bayesian statistical decision theory |
| ISBN |
1-282-34942-2
9786612349423 0-470-74278-X 0-470-74281-X |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Bayesian Analysis of Gene Expression Data; Contents; Table of Notation; 1 Bioinformatics and Gene Expression Experiments; 1.1 Introduction; 1.2 About This Book; 2 Gene Expression Data: Basic Biology and Experiments; 2.1 Background Biology; 2.1.1 DNA Structures and Transcription; 2.2 Gene Expression Microarray Experiments; 2.2.1 Microarray Designs; 2.2.2 Work Flow; 2.2.3 Data Cleaning; 3 Bayesian Linear Models for Gene Expression; 3.1 Introduction; 3.2 Bayesian Analysis of a Linear Model; 3.2.1 Analysis via Conjugate Priors; 3.2.2 Bayesian Variable Selection; 3.2.3 Model Selection Priors
3.2.4 Priors on Regression Coefficients3.2.5 Sparsity Priors; 3.3 Bayesian Linear Models for Differential Expression; 3.3.1 Relevant Work; 3.4 Bayesian ANOVA for Gene Selection; 3.4.1 The Basic Bayesian ANOVA Model; 3.4.2 Differential Expression via Model 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; 4.2.1 Theoretical Developments 4.2.2 FDR Analysis with Gene Expression Arrays4.3 Bayesian False Discovery Rate Analysis; 4.3.1 Theoretical Developments; 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; 5.4.1 Bayesian Analysis of Generalized Linear Models; 5.4.2 Link Functions; 5.4.3 GLM using Latent Processes; 5.4.4 Priors and Computation 5.4.5 Bayesian Probit Regression using Auxiliary Variables5.5 Bayesian Nonlinear Classification; 5.5.1 Classification using Interactions; 5.5.2 Classification using Kernel Methods; 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; 6.2.1 Enrichment Analysis; 6.3 Bayesian Enrichment Analysis; 6.4 Multivariate Gene Class Detection; 6.4.1 Extending the Bayesian ANOVA Model; 6.4.2 Bayesian Decomposition; 6.5 Summary; 7 Unsupervised Classification and Bayesian Clustering 7.1 Introduction to Bayesian Clustering for Gene Expression Data7.2 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; 8.4.1 Multinomial-Dirichlet Model; 8.4.2 Gaussian Model 8.4.3 Model Search |
| Record Nr. | UNINA-9910877239703321 |
| Hoboken, N.J., : Wiley, 2009 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
