Big Data in Omics and Imaging

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 the 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 newly identified genetic variants is small, and a large fraction of genetic variants is still hidden. Understanding the aetiology and causal chain of mechanisms 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, shifting the current paradigm 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.
• Introduces causal inference theory to genomic, epigenomic and imaging data analysis.
• Develops novel statistics for genome-wide causation studies and epigenome-wide causation studies.
• Bridges the gap between traditional association analysis and modern causation analysis.
• Uses combinatorial optimisation methods and various causal models as a general framework for inferring multilevel omic and image causal networks.
• Presents statistical methods and computational algorithms for searching causal paths from genetic variants to disease.
• Develops causal machine learning methods integrating causal inference and machine learning.
• Develops statistics for testing significant differences in directed edges, paths, and graphs, and for assessing causal relationships between two networks.

The book is designed for graduate students and researchers in genomics, epigenomics, medical imaging, bioinformatics, and data science. Topics covered include: 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, and integrated multilevel causal genomic, epigenomic and imaging data analysis.