Big Data in Omics and Imaging

Integrated Analysis and Causal Inference
 
 
Chapman and Hall (Verlag)
  • erschienen am 14. Juni 2018
  • |
  • 766 Seiten
 
E-Book | PDF ohne DRM | Systemvoraussetzungen
978-1-351-17263-9 (ISBN)
 
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.





FEATURES











Provides 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 analysis







Develop novel statistics for genome-wide causation studies and epigenome-wide causation studies.







Bridge the gap between the traditional association analysis and modern causation analysis







Use combinatorial optimization methods and various causal models as a general framework for inferring multilevel omic and image causal networks







Present statistical methods and computational algorithms for searching causal paths from genetic variant to disease







Develop causal machine learning methods integrating causal inference and machine learning







Develop 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.
  • Englisch
  • London
  • |
  • Großbritannien
Taylor & Francis Ltd
  • Für höhere Schule und Studium
40 schwarz-weiße Abbildungen, 30 schwarz-weiße Tabellen
978-1-351-17263-9 (9781351172639)

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.

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 Networks


Directed Acyclic Graphs


Linear Structural Equation Models


Estimation 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 Calculus


Effect 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 Evaluation


Application to Real Data Examples


Appendix 1A


Appendix 1B


Exercises


Figure Legend








2 Causal analysis and network biology


Bayesian 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 Variable


Two-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 Networks


Other Statistical Models for Quantifying Node Score Function


Integer Programming for Causal Structure Leaning


Introduction


Integer Linear Programming Formulation of DAG Learning


Cutting Plane for Integer Linear Programming


Branch and Cut Algorithm for Integer Linear Programming


Sink Finding Primal Heuristic Algorithm


Simulations and Real Data Analysis


Simulations


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 Variables


Appendix 2B Penalized Likelihood Function for Jointly Observational and Interventional Data


Exercises


Figure Legend








3. Wearable Computing and Genetic Analysis of Function-valued Traits


Classification of Wearable Biosensor Data


Introduction


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


Introduction


Functional 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 Traits


Introduction


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 Pass


Backpropagation Pass


Convolutional Layer


Exercises








4. RNA-seq Data Analysis


Normalization Methods on RNA-seq Data Analysis


Gene 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 Networks


Co-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 Networks


Dynamic Bayesian Network and Longitudinal Expression Data Analysis


Single Cell RNA-Seq Data Analysis, Gene Expression Deconvolution and Genetic Screening


Cell Type Identification


Gene Expression Deconvolution and Cell Type-Specific Expression


Figure Legend


Software Package


Appendix 4.1A Variational Bayesian Theory for Parameter Estimation and RNA-Seq


Normalization


Variational Methods for expectation-maximization (EM) algorithm


Variational Methods for Bayesian Learning


Appendix 4.2A Log-linear Model for Differential Expression Analysis of the RNA-Seq Data with Negative Binomial Distribution


Appendix 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 Models


Appendix 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 Test


Set-based Methods


Epigenome-wide Causal Studies


Introduction


Additive Functional Model for EWCS


Genome-wide DNA Methylation Quantitative Trait Locus (mQTL) Analysis


Causal 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 Genomics


Introduction


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 645


Formulation


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 Package


Appendix 6A Factor Graphs and Mean Field Methods for Prediction of Marginal Distribution


Exercises








7. From Association Analysis to Integrated Causal Inference


Genome-wide Causal Studies


Mathematical 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


























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