Computational and statistical epigenomics. ([2015])

• Record Type: Book
• Further Information: Note: Andrew E. Teschendorff, editor.
• Editors: Teschendorff, Andrew E (Andrew Erich)
• Contents: Part I Normalization and Analysis Methods for DNA Methylation and ChIP-Seq Data; 1 Introduction to Data Types in Epigenomics; 1.1 Epigenomics; 1.2 DNA Methylation; 1.2.1 Introduction to DNA Methylation; 1.2.2 The Axes of DNA Methylation Variability; 1.2.3 Methods for DNA Methylation Profiling; 1.3 Bisulfite Microarrays; 1.4 Bisulfite Sequencing; 1.5 Histone Modifications; 1.5.1 Introduction to Histone Modifications; 1.5.2 Profiling Histone Modifications: Experimental Protocol and Data Analysis; 1.5.2.1 Protocol; 1.5.2.2 Data Analysis; 1.6 Repositories and Other Resources. 1.7 ConclusionsReferences; 2 DNA Methylation and Cell-Type Distribution; 2.1 Introduction; 2.2 Fundamental Concepts; 2.3 Reference-Based Methods; 2.4 Reference-Free Methods; 2.5 Data Examples; 2.6 Conclusions; References; 3 A General Strategy for Inter-sample Variability Assessment and Normalisation; 3.1 Introduction; 3.2 Estimating the Dimensionality of Your Data Matrix; 3.3 Assessing the Sources of Inter-sample Variation: The SVD Heatmap; 3.4 Inter-sample Normalisation Methods; 3.5 A Case Study: An EWAS for Smoking in Blood Tissue; 3.6 Conclusions; References. 4 Quantitative Comparison of ChIP-Seq Data Sets Using MAnorm4.1 Introduction; 4.2 Work Flow of MAnorm Model; 4.3 Use MAnorm to Perform Quantitative Comparison of ChIP-Seq Data Sets; 4.3.1 Compare ChIP-Seq Data Sets of Histone Marks Between Different Cell Types; 4.3.2 Identification of Cell-Type-Specific Regulators Associated with Differential Binding; 4.3.3 Use MAnorm to Integrate ChIP-Seq Replicates; 4.4 Performance Comparison Between MAnorm and Other Existing Methods; 4.4.1 Compare the Performance in Inferring Quantitative Changes of ChIP-Seq Signals. 4.4.2 Compare the Performance in Detecting Differential Binding Regions4.5 Define High-Confidence Cell-Type-Specific and Nonspecific Enhancers Using MAnorm; 4.6 Summary and Discussion; References; 5 Model-Based Clustering of DNA Methylation Array Data; 5.1 Introduction; 5.2 Overview of Illumina Infinium DNA Methylation Array Data; 5.3 WBC DNA Methylation Data Set; 5.4 Methods for Model-Based Clustering of DNA Methylation Array Data; 5.4.1 Model-Based Clustering via Finite Mixture Models; 5.4.1.1 The Expectation-Maximization Algorithm; 5.4.1.2 Parameterization of the Covariance Matrix k. 5.4.1.3 Model Selection in Model-Based Clustering Analysis: Determining Covariance Parameterization and the Number of Clusters K5.4.1.4 Application of Mclust in R; 5.4.2 LumiWCluster for Model-Based Clustering Analysis; 5.4.2.1 Application of LumiWCluster in R; 5.4.3 Recursively Partitioned Mixture Models (RPMM); 5.4.3.1 Application of RPMM in R; 5.4.3.2 Assessing the Similarity Between Clustering Partitions; 5.5 Feature Selection in Clustering Analysis; 5.6 Chapter Summary and Discussion; References; Part II Integrative and Medical Epigenomics; 6 Integrative Epigenomics; 6.1 Introduction.
• Publisher Details: Dordrecht [Netherlands] : Springer
• Publication Date: 2015
• Extent: 1 online resource
• Subjects: 616.042; Epigenetics -- Mathematical models; HEALTH & FITNESS -- Diseases -- General; MEDICAL -- Clinical Medicine; MEDICAL -- Diseases; MEDICAL -- Evidence-Based Medicine; MEDICAL -- Internal Medicine; Bioinformatics; Biology -- Data processing; Epidemiology; Life sciences; Medicine; Epigenesis, Genetic; Epigenomics; Statistics as Topic; Life Sciences; Bioinformatics; Computer Appl. in Life Sciences; Molecular Medicine; Epidemiology; Electronic books
• Languages: English
• ISBNs: 9789401799270; 9401799261; 9789401799263
• Related ISBNs: 940179927X; 9789401799263
• Notes: Note: Includes bibliographical references.; Note: Vendor-supplied metadata.
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