Big Data in Omics and Imaging Integrated Analysis and Causal Inference

Big Data in Omics and Imaging: Integrated Analysis and Causal Inference addresses the recent development of integrated genomic, epigenomic and imaging data analysis and causal inference in big data era. Despite significant progress in dissecting the genetic architecture of complex diseases by genome-wide association studies (GWAS), genome-wide expression studies (GWES), and epigenome-wide association studies (EWAS), the overall contribution of the new identified genetic variants is small and a large fraction of genetic variants is still hidden. Understanding the etiology and causal chain of mechanism underlying complex diseases remains elusive. It is time to bring big data, machine learning and causal revolution to developing a new generation of genetic analysis for shifting the current paradigm of genetic analysis from shallow association analysis to deep causal inference and from genetic analysis alone to integrated omics and imaging data analysis for unraveling the mechanism of complex diseases. FEATURESProvides a natural extension and companion volume to Big Data in Omic and Imaging: Association Analysis, but can be read independently.Introduce causal inference theory to genomic, epigenomic and imaging data analysisDevelop novel statistics for genome-wide causation studies and epigenome-wide causation studies.Bridge the gap between the traditional association analysis and modern causation analysisUse combinatorial optimization methods and various causal models as a general framework for inferring multilevel omic and image causal networksPresent statistical methods and computational algorithms for searching causal paths from genetic variant to diseaseDevelop causal machine learning methods integrating causal inference and machine learningDevelop statistics for testing significant difference in directed edge, path, and graphs, and for assessing causal relationships between two networks The book is designed for graduate students and researchers in genomics, epigenomics, medical image, bioinformatics, and data science. Topics covered are: mathematical formulation of causal inference, information geometry for causal inference, topology group and Haar measure, additive noise models, distance correlation, multivariate causal inference and causal networks, dynamic causal networks, multivariate and functional structural equation models, mixed structural equation models, causal inference with confounders, integer programming, deep learning and differential equations for wearable computing, genetic analysis of function-valued traits, RNA-seq data analysis, causal networks for genetic methylation analysis, gene expression and methylation deconvolution, cell –specific causal networks, deep learning for image segmentation and image analysis, imaging and genomic data analysis, integrated multilevel causal genomic, epigenomic and imaging data analysis.

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 St

Momiao Xiong is a professor of Biostatistics at the University of Texas Health Science Center in Houston where he has worked since 1997. He received his PhD in 1993 from the University of Georgia.