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E-raamat: Analyzing High-Dimensional Gene Expression and DNA Methylation Data with R [Taylor & Francis e-raamat]

(University of Memphis, Tennessee, USA)
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Analyzing High-Dimensional Gene Expression and DNA Methylation Data with RSuurem pilt
Teised raamatud teemal:
Taylor & Francis e-raamatudRohkem infot Taylor & Francis e-raamatute kohta
Raamatu kodulehekülg: https://www.taylorfrancis.com/books/9780429155192

Analyzing high-dimensional gene expression and DNA methylation data with R is the first practical book that shows a ``pipeline" of analytical methods with concrete examples starting from raw gene expression and DNA methylation data at the genome scale. Methods on quality control, data pre-processing, data mining, and further assessments are presented in the book, and R programs based on simulated data and real data are included. Codes with example data are all reproducible.

Features:

·         Provides a sequence of analytical tools for genome-scale gene expression data and DNA methylation data, starting from quality control and pre-processing of raw genome-scale data.

·         Organized by a parallel presentation with explanation on statistical methods and corresponding R packages/functions in quality control, pre-processing, and data analyses (e.g., clustering and networks).

·         Includes source codes with simulated and real data to reproduce the results. Readers are expected to gain the ability to independently analyze genome-scaled expression and methylation data and detect potential biomarkers.

 

This book is ideal for students majoring in statistics, biostatistics, and bioinformatics and researchers with an interest in high dimensional genetic and epigenetic studies.

Preface xi
Chapter 1 Introduction
1(4)
1.1 Pipelines To Analyze "Omics" Data
1(2)
1.2 Rna-Seq Gene Expression In S2-Drsc Cells
3(1)
1.3 Microarray Gene Expression In Yeast Cells And In Prostate Samples
3(1)
1.4 Dna Methylation In Normal And Colon/Rectal Adenocarcinoma Samples
4(1)
Chapter 2 Genome-scale gene expression data
5(12)
2.1 Microarray Gene Expression Data
5(8)
2.1.1 Data generation
5(2)
2.1.2 Preprocessing and quality control of microarray data
7(6)
2.2 Data From Next Generation Sequencing
13(4)
2.2.1 Data generation
14(1)
2.2.2 Preprocessing and quality control of bulk RNA-Seq data
14(3)
Chapter 3 Genome-scale epigenetic data
17(22)
3.1 Data Generation
17(1)
3.2 Quality Control And Preprocessing Of Dna Methylation Data
18(4)
3.2.1 The control probe adjustment and reduction of global correlation pipeline (CPACOR)
18(3)
3.2.2 Quantile normalization with ComBat
21(1)
3.3 Cell Type Composition Inferences
22(9)
3.3.1 Reference-based methods
23(4)
3.3.2 Reference-free methods
27(4)
3.4 Appendix - Modified Programs In The Cpacor With An Application
31(8)
Chapter 4 Screening genome-scale genetic and epigenetic date
39(18)
4.1 Screening Via Training And Testing Samples
40(1)
4.2 Screening Incorporating Surrogate Variables
41(5)
4.3 Sure Independence Screening
46(4)
4.3.1 Correlation learning
47(3)
4.4 Non- And Semi-Parametric Screening Techniques
50(7)
4.4.1 Random forest
50(3)
4.4.2 Support vector machine
53(4)
Chapter 5 Cluster Analysis in Data mining
57(44)
5.1 Non-Parametric Cluster Analysis Methods
57(28)
5.1.1 Distances
58(1)
5.1.2 Partitioning-based methods
59(5)
5.1.3 Hierarchical clustering
64(5)
5.1.4 Hybrids of partitioning-based and hierarchical clustering
69(6)
5.1.5 Examples - clustering to detect gene expression patterns
75(10)
5.2 Cluster Analyses In Linear Regressions
85(6)
5.3 Bicluster Analyses
91(5)
5.4 Joint Cluster Analysis
96(5)
Chapter 6 Methods to select genetic and epigenetic factors based on linear associations
101(24)
6.1 Frequentist Approaches
102(6)
6.1.1 Elastic net
102(3)
6.1.2 Adaptive LASSO
105(1)
6.1.3 Smoothly clipped absolute deviation (SCAD)
106(2)
6.2 Bayesian Approaches
108(10)
6.2.1 Zellner's g-prior
109(2)
6.2.2 Extension of Zellner's g-prior to multi-components G-prior
111(2)
6.2.3 The spike-and-slab prior
113(5)
6.3 Examples - Selecting Important Epigenetic Factors
118(7)
Chapter 7 Non- and semi-parametric methods to select genetic and epigenetic factors
125(20)
7.1 Variable Selection Based On Splines
126(2)
7.2 Overview Of The Anova-Based Approach
128(1)
7.3 Variable Selection Built Upon Reproducing Kernels
129(3)
7.4 Examples
132(13)
7.4.1 Selecting important epigenetic factors
132(7)
7.4.2 Selecting variables with known underlying truth
139(6)
Chapter 8 Network construction and analyses
145(20)
8.1 Undirected Networks
145(7)
8.1.1 The two-stage graphs selection method
146(1)
8.1.2 The GGMselect package and gene expression examples
147(5)
8.2 Correlation Networks
152(6)
8.3 Bayesian Networks
158(3)
8.4 Network Comparisons
161(4)
8.4.1 Comparing undirected networks
162(1)
8.4.2 Comparing Bayesian networks
163(2)
Bibliography 165(18)
Index 183
Hongmei Zhang is a Biostatistician at the University of Memphis. She has been working with gene expression and DNA methylation data and her methodological research interest is to develop corresponding statistical methods. She has been teaching courses in this field for a number of years.
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