Wong, Ka-Chun.

Big Data Analytics in Genomics. - Cham : Springer International Publishing, 2016. - 1 online resource (426 p.) - eBooks on Demand .

Preface -- Contents -- Part I Statistical Analytics -- Introduction to Statistical Methods for Integrative Data Analysis in Genome-Wide Association Studies -- 1 Introduction -- 2 Heritability Estimation -- 2.1 The Basic Idea of Heritability Estimation from Pedigree Data -- 2.2 Heritability Estimation Based on GWAS -- 3 Integrative Analysis of Multiple GWAS -- 4 Integrative Analysis of GWAS with Functional Information -- 5 Case Study -- 6 Future Directions and Conclusion -- References -- Robust Methods for Expression Quantitative Trait Loci Mapping -- 1 Introduction -- 2 eQTL Mapping 2.1 Group-Wise eQTL Mapping and Challenges -- 2.2 Overview of the Developed Algorithms -- 2.3 Chapter Outline -- 3 Group-Wise eQTL Mapping -- 3.1 Introduction -- 3.2 Related Work -- 3.3 The Problem -- 3.4 Detecting Group-Wise Associations -- 3.4.1 SET-eQTL Model -- 3.4.2 Objective Function -- 3.5 Considering Confounding Factors -- 3.6 Incorporating Individual Effect -- 3.6.1 Objective Function -- 3.6.2 Increasing Computational Speed -- Updating σ2 -- Efficiently Inverting the Covariance Matrix -- 3.7 Optimization -- 3.8 Experimental Results -- 3.8.1 Simulation Study -- Shrinkage of C and BA Computational Efficiency Evaluation -- 3.8.2 Yeast eQTL Study -- cis- and trans-Enrichment Analysis -- Reproducibility of trans Regulatory Hotspots Between Studies -- Gene Ontology Enrichment Analysis -- 3.9 Conclusion -- 4 Incorporating Prior Knowledge for Robust eQTL Mapping -- 4.1 Introduction -- 4.2 Background: Linear Regression with Graph Regularizer -- 4.2.1 Lasso and LORS -- 4.2.2 Graph-Regularized Lasso -- 4.3 Graph-Regularized Dual Lasso -- 4.3.1 Optimization: An Alternating Minimization Approach -- 4.3.2 Convergence Analysis -- 4.4 Generalized Graph-Regularized Dual Lasso 4.5 Experimental Results -- 4.5.1 Simulation Study -- 4.5.2 Yeast eQTL Study -- 4.5.3 cis and trans Enrichment Analysis -- 4.5.4 Refinement of the Prior Networks -- Hotspots Analysis -- 4.6 Conclusion -- 5 Discussion -- 5.1 Summary -- 5.2 Future Directions -- References -- Causal Inference and Structure Learning of Genotype-Phenotype Networks Using Genetic Variation -- 1 Introduction -- 2 Mendelian Randomization -- 2.1 Randomized Controlled Trial -- 2.2 Randomized Allocation of Allelic Variation in Genes -- 2.3 Genetic Variants as Instrumental Variables 2.3.1 Statistical Association with the Exposure -- 2.3.2 Independence with Exposure-Outcome Confounders -- 2.3.3 Exclusion Restriction -- 3 Causal Model -- 3.1 Functional Causal Representation -- 3.2 Graphical Causal Representation -- 4 Properties Relating Functional and Graphical Models -- 4.1 d-Separability -- 4.2 Global Directed Markov Property -- 4.2.1 Local Directed Markov Property in DAGs -- 4.3 Causal Faithfulness -- 4.4 Factorization of Joint Probability Distribution Functions -- 4.4.1 Factorization and Global Markov Property -- 4.5 Linear Entailment and Partial Correlations 5 Equivalent Models

9783319412795 129 (NL),129 (1U)


Big data.


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