Ebook: Principles of Statistical Genomics

Statistical genomics is a rapidly developing field, with more and more people involved in this area. However, a lack of synthetic reference books and textbooks in statistical genomics has become a major hurdle on the development of the field. Although many books have been published recently in bioinformatics, most of them emphasize DNA sequence analysis under a deterministic approach.

Principles of Statistical Genomics synthesizes the state-of-the-art statistical methodologies (stochastic approaches) applied to genome study. It facilitates understanding of the statistical models and methods behind the major bioinformatics software packages, which will help researchers choose the optimal algorithm to analyze their data and better interpret the results of their analyses. Understanding existing statistical models and algorithms assists researchers to develop improved statistical methods to extract maximum information from their data.

Resourceful and easy to use, Principles of Statistical Genomics is a comprehensive reference for researchers and graduate students studying statistical genomics.

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Statistical genomics is a rapidly developing field, with more and more people involved in this area. However, a lack of synthetic reference books and textbooks in statistical genomics has become a major hurdle to the development of the field. Although many books have been published recently in bioinformatics, most of them emphasize DNA sequence analysis under a deterministic approach.

Principles of Statistical Genomics synthesizes the state-of-the-art statistical methodologies (stochastic approaches) applied to genome study. It facilitates understanding of the statistical models and methods behind the major bioinformatics software packages, which will help researchers choose the optimal algorithm to analyze their data and better interpret the results of their analyses. Understanding existing statistical models and algorithms assists researchers to develop improved statistical methods to extract maximum information from their data.

Resourceful and easy to use, Principles of Statistical Genomics is a comprehensive reference for researchers and graduate students studying statistical genomics.

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Statistical genomics is a rapidly developing field, with more and more people involved in this area. However, a lack of synthetic reference books and textbooks in statistical genomics has become a major hurdle to the development of the field. Although many books have been published recently in bioinformatics, most of them emphasize DNA sequence analysis under a deterministic approach.

Principles of Statistical Genomics synthesizes the state-of-the-art statistical methodologies (stochastic approaches) applied to genome study. It facilitates understanding of the statistical models and methods behind the major bioinformatics software packages, which will help researchers choose the optimal algorithm to analyze their data and better interpret the results of their analyses. Understanding existing statistical models and algorithms assists researchers to develop improved statistical methods to extract maximum information from their data.

Resourceful and easy to use, Principles of Statistical Genomics is a comprehensive reference for researchers and graduate students studying statistical genomics.

Content:
Front Matter....Pages i-xv
Front Matter....Pages 1-1
Map Functions....Pages 3-10
Recombination Fraction....Pages 11-22
Genetic Map Construction....Pages 23-33
Multipoint Analysis of Mendelian Loci....Pages 35-49
Front Matter....Pages 51-51
Basic Concepts of Quantitative Genetics....Pages 53-60
Major Gene Detection....Pages 61-78
Segregation Analysis....Pages 79-93
Genome Scanning for Quantitative Trait Loci....Pages 95-108
Interval Mapping....Pages 109-129
Interval Mapping for Ordinal Traits....Pages 131-149
Mapping Segregation Distortion Loci....Pages 151-170
QTL Mapping in Other Populations....Pages 171-185
Random Model Approach to QTL Mapping....Pages 187-207
Mapping QTL for Multiple Traits....Pages 209-222
Bayesian Multiple QTL Mapping....Pages 223-256
Empirical Bayesian QTL Mapping....Pages 257-279
Front Matter....Pages 281-281
Microarray Differential Expression Analysis....Pages 283-302
Hierarchical Clustering of Microarray Data....Pages 303-319
Model-Based Clustering of Microarray Data....Pages 321-333
Gene-Specific Analysis of Variances....Pages 335-342
Front Matter....Pages 281-281
Factor Analysis of Microarray Data....Pages 343-353
Classification of Tissue Samples Using Microarrays....Pages 355-363
Time-Course Microarray Data Analysis....Pages 365-382
Quantitative Trait-Associated Microarray Data Analysis....Pages 383-394
Mapping Expression Quantitative Trait Loci....Pages 395-411
Back Matter....Pages 413-428

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Statistical genomics is a rapidly developing field, with more and more people involved in this area. However, a lack of synthetic reference books and textbooks in statistical genomics has become a major hurdle to the development of the field. Although many books have been published recently in bioinformatics, most of them emphasize DNA sequence analysis under a deterministic approach.

Principles of Statistical Genomics synthesizes the state-of-the-art statistical methodologies (stochastic approaches) applied to genome study. It facilitates understanding of the statistical models and methods behind the major bioinformatics software packages, which will help researchers choose the optimal algorithm to analyze their data and better interpret the results of their analyses. Understanding existing statistical models and algorithms assists researchers to develop improved statistical methods to extract maximum information from their data.

Resourceful and easy to use, Principles of Statistical Genomics is a comprehensive reference for researchers and graduate students studying statistical genomics.

Content:
Front Matter....Pages i-xv
Front Matter....Pages 1-1
Map Functions....Pages 3-10
Recombination Fraction....Pages 11-22
Genetic Map Construction....Pages 23-33
Multipoint Analysis of Mendelian Loci....Pages 35-49
Front Matter....Pages 51-51
Basic Concepts of Quantitative Genetics....Pages 53-60
Major Gene Detection....Pages 61-78
Segregation Analysis....Pages 79-93
Genome Scanning for Quantitative Trait Loci....Pages 95-108
Interval Mapping....Pages 109-129
Interval Mapping for Ordinal Traits....Pages 131-149
Mapping Segregation Distortion Loci....Pages 151-170
QTL Mapping in Other Populations....Pages 171-185
Random Model Approach to QTL Mapping....Pages 187-207
Mapping QTL for Multiple Traits....Pages 209-222
Bayesian Multiple QTL Mapping....Pages 223-256
Empirical Bayesian QTL Mapping....Pages 257-279
Front Matter....Pages 281-281
Microarray Differential Expression Analysis....Pages 283-302
Hierarchical Clustering of Microarray Data....Pages 303-319
Model-Based Clustering of Microarray Data....Pages 321-333
Gene-Specific Analysis of Variances....Pages 335-342
Front Matter....Pages 281-281
Factor Analysis of Microarray Data....Pages 343-353
Classification of Tissue Samples Using Microarrays....Pages 355-363
Time-Course Microarray Data Analysis....Pages 365-382
Quantitative Trait-Associated Microarray Data Analysis....Pages 383-394
Mapping Expression Quantitative Trait Loci....Pages 395-411
Back Matter....Pages 413-428
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