+44 1803 865913
By: Shizhong Xu(Author)
443 pages, 12 colour & 34 b/w illustrations, 29 tables
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.
Part I Genetic Linkage Map
1 Map Functions
2 Recombination Fraction
3 Genetic Map Construction
4 Multipoint Analysis of Mendelian Loci
Part II Analysis of Quantitative Traits
5 Basic Concepts of Quantitative Genetics
6 Major Gene Detection
7 Segregation Analysis
8 Genome Scanning for Quantitative Trait Loci
9 Interval Mapping
10 Interval Mapping for Ordinal Traits
11 Mapping Segregation Distortion Loci
12 QTL Mapping in Other Populations
13 Random Model Approach to QTL Mapping
14 Mapping QTL for Multiple Traits
15 Bayesian Multiple QTL Mapping
16 Empirical Bayesian QTL Mapping
Part III Microarray Data Analysis
17 Microarray Differential Expression Analysis
18 Hierarchical Clustering of Microarray Data
19 Model-Based Clustering of Microarray Data
20 Gene Specific Analysis of Variances
21 Factor Analysis of Microarray Data
22 Classification of Tissue Samples Using Microarrays
23 Time-Course Microarray Data Analysis
24 Quantitative Trait Associated Microarray Data Analysis
25 Mapping Expression Quantitative Trait Loci
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