Ebook: Modeling Dose-Response Microarray Data in Early Drug Development Experiments Using R: Order-Restricted Analysis of Microarray Data

This book focuses on the analysis of dose-response microarray data in pharmaceutical settings, the goal being to cover this important topic for early drug development experiments and to provide user-friendly R packages that can be used to analyze this data. It is intended for biostatisticians and bioinformaticians in the pharmaceutical industry, biologists, and biostatistics/bioinformatics graduate students.

Part I of the book is an introduction, in which we discuss the dose-response setting and the problem of estimating normal means under order restrictions. In particular, we discuss the pooled-adjacent-violator (PAV) algorithm and isotonic regression, as well as inference under order restrictions and non-linear parametric models, which are used in the second part of the book.

Part II is the core of the book, in which we focus on the analysis of dose-response microarray data. Methodological topics discussed include:

• Multiplicity adjustment

• Test statistics and procedures for the analysis of dose-response microarray data

• Resampling-based inference and use of the SAM method for small-variance genes in the data

• Identification and classification of dose-response curve shapes

• Clustering of order-restricted (but not necessarily monotone) dose-response profiles

• Gene set analysis to facilitate the interpretation of microarray results

• Hierarchical Bayesian models and Bayesian variable selection

• Non-linear models for dose-response microarray data

• Multiple contrast tests

• Multiple confidence intervals for selected parameters adjusted for the false coverage-statement rate

All methodological issues in the book are illustrated using real-world examples of dose-response microarray datasets from early drug development experiments.

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This book focuses on the analysis of dose-response microarray data in pharmaceutical setting, the goal being to cover this important topic for early drug development and to provide user-friendly R packages that can be used to analyze dose-response microarray data. It is intended for biostatisticians and bioinformaticians in the pharmaceutical industry, biologists, and biostatistics/bioinformatics graduate students.

Part I of the book is an introduction, in which we discuss the dose-response setting and the problem of estimating normal means under order restrictions. In particular, we discuss the pooled-adjacent-violator (PAV) algorithm and isotonic regression, as well as the likelihood ratio test and non-linear parametric models, which are used in the second part of the book.

Part II is the core of the book. Methodological topics discussed include:

· Multiplicity adjustment

· Test statistics and testing procedures for the analysis of dose-response microarray data

· Resampling-based inference and use of the SAM method at the presence of small-variance genes in the data

· Identification and classification of dose-response curve shapes

· Clustering of order restricted (but not necessarily monotone) dose-response profiles

· Hierarchical Bayesian models and non-linear models for dose-response microarray data

· Multiple contrast tests

All methodological issues in the book are illustrated using four “real-world” examples of dose-response microarray datasets from early drug development experiments.

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This book focuses on the analysis of dose-response microarray data in pharmaceutical setting, the goal being to cover this important topic for early drug development and to provide user-friendly R packages that can be used to analyze dose-response microarray data. It is intended for biostatisticians and bioinformaticians in the pharmaceutical industry, biologists, and biostatistics/bioinformatics graduate students.

Part I of the book is an introduction, in which we discuss the dose-response setting and the problem of estimating normal means under order restrictions. In particular, we discuss the pooled-adjacent-violator (PAV) algorithm and isotonic regression, as well as the likelihood ratio test and non-linear parametric models, which are used in the second part of the book.

Part II is the core of the book. Methodological topics discussed include:

· Multiplicity adjustment

· Test statistics and testing procedures for the analysis of dose-response microarray data

· Resampling-based inference and use of the SAM method at the presence of small-variance genes in the data

· Identification and classification of dose-response curve shapes

· Clustering of order restricted (but not necessarily monotone) dose-response profiles

· Hierarchical Bayesian models and non-linear models for dose-response microarray data

· Multiple contrast tests

All methodological issues in the book are illustrated using four “real-world” examples of dose-response microarray datasets from early drug development experiments.
Content:
Front Matter....Pages i-xv
Front Matter....Pages 9-9
Introduction....Pages 1-7
Estimation Under Order Restrictions....Pages 11-27
Testing of Equality of Means Against Ordered Alternatives....Pages 29-42
Nonlinear Modeling of Dose-Response Data....Pages 43-66
Front Matter....Pages 67-67
Functional Genomic Dose-Response Experiments....Pages 69-80
Adjustment for Multiplicity....Pages 81-101
Single Contrast Tests....Pages 103-121
Significance Analysis of Dose-Response Microarray Data....Pages 123-133
?-Clustering of Monotone Profiles....Pages 135-149
Classification of Monotone Gene Profiles Using Information Theory Selection Methods....Pages 151-163
Beyond the Simple Order Alternatives....Pages 165-180
Gene Set Analysis as a Means of Facilitating the Interpretation of Microarray Results....Pages 181-191
Estimation and Inference Under Simple Order Restrictions: Hierarchical Bayesian Approach....Pages 193-214
Model-Based Approaches....Pages 215-232
Multiple Contrast Tests for Testing Dose–Response Relationships Under Order-Restricted Alternatives....Pages 233-247
Simultaneous Inferences for Ratio Parameters Using Multiple Contrasts Test....Pages 249-258
Multiple Confidence Intervals for Selected Ratio Parameters Adjusted for the False Coverage-Statement Rate....Pages 259-267
Interfaces for Analyzing Dose–Response Studies in Microarray Experiments: IsoGeneGUI and ORIOGEN....Pages 269-279
Back Matter....Pages 281-282

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This book focuses on the analysis of dose-response microarray data in pharmaceutical setting, the goal being to cover this important topic for early drug development and to provide user-friendly R packages that can be used to analyze dose-response microarray data. It is intended for biostatisticians and bioinformaticians in the pharmaceutical industry, biologists, and biostatistics/bioinformatics graduate students.

Part I of the book is an introduction, in which we discuss the dose-response setting and the problem of estimating normal means under order restrictions. In particular, we discuss the pooled-adjacent-violator (PAV) algorithm and isotonic regression, as well as the likelihood ratio test and non-linear parametric models, which are used in the second part of the book.

Part II is the core of the book. Methodological topics discussed include:

· Multiplicity adjustment

· Test statistics and testing procedures for the analysis of dose-response microarray data

· Resampling-based inference and use of the SAM method at the presence of small-variance genes in the data

· Identification and classification of dose-response curve shapes

· Clustering of order restricted (but not necessarily monotone) dose-response profiles

· Hierarchical Bayesian models and non-linear models for dose-response microarray data

· Multiple contrast tests

All methodological issues in the book are illustrated using four “real-world” examples of dose-response microarray datasets from early drug development experiments.
Content:
Front Matter....Pages i-xv
Front Matter....Pages 9-9
Introduction....Pages 1-7
Estimation Under Order Restrictions....Pages 11-27
Testing of Equality of Means Against Ordered Alternatives....Pages 29-42
Nonlinear Modeling of Dose-Response Data....Pages 43-66
Front Matter....Pages 67-67
Functional Genomic Dose-Response Experiments....Pages 69-80
Adjustment for Multiplicity....Pages 81-101
Single Contrast Tests....Pages 103-121
Significance Analysis of Dose-Response Microarray Data....Pages 123-133
?-Clustering of Monotone Profiles....Pages 135-149
Classification of Monotone Gene Profiles Using Information Theory Selection Methods....Pages 151-163
Beyond the Simple Order Alternatives....Pages 165-180
Gene Set Analysis as a Means of Facilitating the Interpretation of Microarray Results....Pages 181-191
Estimation and Inference Under Simple Order Restrictions: Hierarchical Bayesian Approach....Pages 193-214
Model-Based Approaches....Pages 215-232
Multiple Contrast Tests for Testing Dose–Response Relationships Under Order-Restricted Alternatives....Pages 233-247
Simultaneous Inferences for Ratio Parameters Using Multiple Contrasts Test....Pages 249-258
Multiple Confidence Intervals for Selected Ratio Parameters Adjusted for the False Coverage-Statement Rate....Pages 259-267
Interfaces for Analyzing Dose–Response Studies in Microarray Experiments: IsoGeneGUI and ORIOGEN....Pages 269-279
Back Matter....Pages 281-282
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