Big Data in Omics and Imaging : Integrated Analysis and Causal Inference (Paperback)

1. Genotype-Phenotype Network Analysis Undirected Graphs for Genotype Network Gaussian Graphic Model Alternating Direction Method of Multipliers for Estimation of Gaussian Graphical Model Coordinate Descent Algorithm and Graphical Lasso Multiple Graphical Models Directed Graphs and Structural Equation Models for NetworksDirected Acyclic Graphs Linear Structural Equation ModelsEstimation Methods Sparse Linear Structural Equations Penalized Maximum Likelihood Estimation Penalized Two Stage Least Square Estimation Penalized Three Stage Least Square Estimation Functional Structural Equation Models for Genotype-Phenotype Networks Functional Structural Equation Models Group Lasso and ADMM for Parameter Estimation in the Functional Structural Equation Models Causal CalculusEffect Decomposition and Estimation Graphical Tools for Causal Inference in Linear SEMs Identification and Single-door Criterion Instrument Variables Total Effects and Backdoor Criterion Counterfactuals and Linear SEMs Simulations and Real Data Analysis Simulations for Model EvaluationApplication to Real Data ExamplesAppendix 1A Appendix 1BExercisesFigure Legend 2 Causal analysis and network biologyBayesian Networks as a General Framework for Causal Inference Parameter Estimation and Bayesian Dirichlet Equivalent Uniform Score for Discrete Bayesian Networks Structural Equations and Score Metrics for Continuous Causal Networks Multivariate SEMs for Generating Node Core Metrics Mixed SEMs for Pedigree-based Causal Inference Bayesian Networks with Discrete and Continuous VariableTwo-class Network Penalized Logistic Regression for Learning Hybrid Bayesian Networks Multiple Network Penalized Functional Logistic Regression Models for NGS Data Multi-class Network Penalized Logistic Regression for Learning Hybrid Bayesian NetworksOther Statistical Models for Quantifying Node Score FunctionInteger Programming for Causal Structure LeaningIntroductionInteger Linear Programming Formulation of DAG LearningCutting Plane for Integer Linear Programming Branch and Cut Algorithm for Integer Linear ProgrammingSink Finding Primal Heuristic Algorithm Simulations and Real Data AnalysisSimulations Real Data Analysis Figure Legend Software Package Appendix 2A Introduction to Smoothing Splines Smoothing Spline Regression for a Single Variable Smoothing Spline Regression for Multiple VariablesAppendix 2B Penalized Likelihood Function for Jointly Observational and Interventional Data Exercises Figure Legend 3. Wearable Computing and Genetic Analysis of Function-valued TraitsClassification of Wearable Biosensor DataIntroduction Functional Data Analysis for Classification of Time Course Wearable Biosensor Data Differential Equations for Extracting Features of the Dynamic Process and for Classification of Time Course Data Deep Learning for Physiological Time Series Data Analysis Association Studies of Function-Valued Traits IntroductionFunctional Linear Models with both Functional Response and Predictors for Association Analysis of Function-valued Traits Test Statistics Null Distribution of Test Statistics Power Real Data Analysis Association Analysis of Multiple Function-valued Traits Gene-gene Interaction Analysis of Function-Valued TraitsIntroduction Functional Regression Models Estimation of Interaction Effect Function Test Statistics Simulations Real Data Analysis Figure Legend Appendix 3.A Gradient Methods for Parameter Estimation in the Convolutional Neural Networks Multilayer Feedforward PassBackpropagation PassConvolutional Layer Exercises4. RNA-seq Data Analysis Normalization Methods on RNA-seq Data AnalysisGene Expression RNA Sequencing Expression Profiling Methods for Normalization Differential Expression Analysis for RNA-Seq Data Distribution-based Approach to Differential Expression Analysis Functional Expansion Approach to Differential Expression Analysis of RNA-Seq Data Differential Analysis of Allele Specific Expressions with RNA-Seq Data eQTL and eQTL Epistasis Analysis with RNA-Seq Data Matrix Factorization Quadratically Regularized Matrix Factorization and Canonical Correlation Analysis QRFCCA for eQTL and eQTL Epistasis Analysis of RNA-Seq Data Real Data Analysis Gene Co-expression Network and Gene Regulatory NetworksCo-expression Network Construction with RNA-Seq Data by CCA and FCCA Graphical Gaussian Models Real Data Applications Directed Graph and Gene Regulatory Networks Hierarchical Bayesian Networks for Whole Genome Regulatory Networks Linear Regulatory Networks Nonlinear Regulatory NetworksDynamic Bayesian Network and Longitudinal Expression Data AnalysisSingle Cell RNA-Seq Data Analysis, Gene Expression Deconvolution and Genetic ScreeningCell Type IdentificationGene Expression Deconvolution and Cell Type-Specific ExpressionFigure Legend Software Package Appendix 4.1A Variational Bayesian Theory for Parameter Estimation and RNA-Seq Normalization Variational Methods for expectation-maximization (EM) algorithmVariational Methods for Bayesian Learning Appendix 4.2A Log-linear Model for Differential Expression Analysis of the RNA-Seq Data with Negative Binomial DistributionAppendix 4.5A Derivation of ADMM Algorithm Appendix 4.5B Low Rank Representation Induced Sparse Structural Equation Models Appendix 4.6A Maximum Likelihood (ML) Estimation of Parameters for Dynamic Structural Equation Models Appendix 4.6B Generalized Least Squares Estimator of The Parameters in Dynamic Structural Equation ModelsAppendix 4.6C Proximal Algorithm for L1-Penalized Maximum Likelihood Estimation of Dynamic Structural Equation Model Appendix 4.6D Proximal Algorithm for L1- Penalized Generalized Least Square Estimation of Parameters in the Dynamic Structural Equation Models Appendix 4.7A Multikernel Learning and Spectral Clustering for Cell Type Identification Exercises 5 Methylation Data Analysis DNA Methylation Analysis Epigenome-wide Association Studies (EWAS)Single-Locus TestSet-based MethodsEpigenome-wide Causal StudiesIntroduction Additive Functional Model for EWCS Genome-wide DNA Methylation Quantitative Trait Locus (mQTL) AnalysisCausal Networks for Genetic-Methylation Analysis Structural Equation Models with Scalar Endogenous Variables and Functional Exogenous Variables Functional Structural Equation Models with Functional Endogenous Variables and Scalar Exogenous Variables (FSEMS) Functional Structural Equation Models with both Functional Endogenous Variables an Exogenous Variables (FSEMF) Figure Legend Software Package Appendix 5A Biased and Unbiased Estimators of the HSIC Appendix 5B Asymptotic Null Distribution of Block-Based HSIC Exercises 6 Imaging and GenomicsIntroduction Image Segmentation Unsupervised Learning Methods for Image Segmentation Supervised Deep Learning Methods for Image Segmentation Two or Three dimensional Functional Principal Component Analysis for Image Data Reduction 645Formulation Integral Equation and Eigenfunctions Association Analysis of Imaging-Genomic Data Multivariate Functional Regression Models for Imaging-Genomic Data Analysis Multivariate Functional Regression Models for Longitudinal Imaging-Genetics Analysis Quadratically Regularized Functional Canonical Correlation Analysis for Gene-Gene Interaction Detection in Imaging-Genetic Studies Causal Analysis of Imaging-Genomic Data Sparse SEMs for Joint Causal Analysis of Structural Imaging and Genomic Data Sparse Functional Structural Equation Models for phenotype and genotype networks. Conditional Gaussian Graphical Models (CGGMs) for Structural Imaging and Genomic Data Analysis. Time Series SEMs for Integrated Causal Analysis of fMRI and Genomic Data Models Reduced Form Equations Single Equation and Generalized Least Square Estimator Sparse SEMs and Alternating Direction Method of Multipliers Causal machine learning Figure Legend Software PackageAppendix 6A Factor Graphs and Mean Field Methods for Prediction of Marginal DistributionExercises7. From Association Analysis to Integrated Causal Inference Genome-wide Causal StudiesMathematical Formulation of Causal Analysis Basic Causal Assumptions Linear Additive SEMs with non-Gaussian Noise Information Geometry Approach Causal Inference on Discrete Data Multivariate Causal Inference and Causal Networks Markov Condition, Markov Equivalence, Faithfulness and Minimality Multilevel Causal Networks for Integrative Omics and Imaging Data Analysis Causal Inference with Confounders Causal Sufficiency Instrumental Variables Figure Legend Software Package Appendix 7A Approximation of log-likelihood Ratio for the LiNGAM Appendix 7B Orthogonality Conditions and Covariance Appendix 7C Equivalent Formulations Orthogonality Conditions Appendix 7D M-L Distance in Backward Direction Appendix 7E Multiplicativity of Traces Appendix 7F Anisotropy and K-L Distance Appendix 7G Trace Method for Noise Linear Model Appendix 7H Characterization of Association Appendix 7I Algorithm for Sparse Trace Method Exercises