Big Data In Omics And Imaging - Xiong Momiao | Libro Chapman And Hall/Crc 12/2017 - HOEPLI.it


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Big Data in Omics and Imaging Association Analysis




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Dettagli

Genere:Libro
Lingua: Inglese
Pubblicazione: 12/2017
Edizione: 1° edizione





Note Editore

Big Data in Omics and Imaging: Association Analysis addresses the recent development of association analysis and machine learning for both population and family genomic data in sequencing era. It is unique in that it presents both hypothesis testing and a data mining approach to holistically dissecting the genetic structure of complex traits and to designing efficient strategies for precision medicine. The general frameworks for association analysis and machine learning, developed in the text, can be applied to genomic, epigenomic and imaging data.

FEATURES

Bridges the gap between the traditional statistical methods and computational tools for small genetic and epigenetic data analysis and the modern advanced statistical methods for big data

Provides tools for high dimensional data reduction

Discusses searching algorithms for model and variable selection including randomization algorithms, Proximal methods and matrix subset selection

Provides real-world examples and case studies

Will have an accompanying website with R code


The book is designed for graduate students and researchers in genomics, bioinformatics, and data science. It represents the paradigm shift of genetic studies of complex diseases– from shallow to deep genomic analysis, from low-dimensional to high dimensional, multivariate to functional data analysis with next-generation sequencing (NGS) data, and from homogeneous populations to heterogeneous population and pedigree data analysis. Topics covered are: advanced matrix theory, convex optimization algorithms, generalized low rank models, functional data analysis techniques, deep learning principle and machine learning methods for modern association, interaction, pathway and network analysis of rare and common variants, biomarker identification, disease risk and drug response prediction.





Sommario

Mathematical Foundation

Sparsity-Inducing Norms, Dual Norms and Fenchel Conjugate

Subdifferential

Definition of Subgradient

Subgradients of differentiable functions

Calculus of subgradients

Proximal Methods

Introduction

Basics of Proximate Methods

Properties of the Proximal Operator

Proximal Algorithms

Computing the Proximal Operator

Matrix Calculus

Derivative of a Function with Respect to a Vector

Derivative of a Function with Respect to a Matrix

Derivative of a Matrix with Respect to a Scalar

Derivative of a Matrix with Respect to a Matrix or a Vector

Derivative of a Vector Function of a Vector

Chain Rules

Widely Used Formulae

Functional Principal Component Analysis (FPCA)

Principal Component Analysis (PCA)

Basic Mathematical Tools for Functional Principal Component Analysis

Unsmoothed Functional Principal Component Analysis

Smoothed Principal Component Analysis

Computations for the Principal Component Function and the Principal Component Score

Canonical Correlation Analysis

Exercises

Appendix

Linkage Disequilibrium

Concepts of Linkage Disequilibrium

Measures of Two-locus Linkage Disequilibrium

Linkage Disequilibrium Coefficient D

Normalized Measure of Linkage Disequilibrium

Correlation Coefficient r

Composite Measure of Linkage Disequilibrium

The Relationship Between the Measure of LD and Physical Distance

Haplotype Reconstruction

Clark’s Algorithm

EM algorithm

Bayesian and Coalescence-based Methods

Multi-locus Measures of Linkage Disequilibrium

Mutual Information Measure of LD

Multi-Information and Multi-locus Measure of LD

Joint Mutual Information and a Measure of LD between a Marker and a Haplotype Block or Between Two Haplotype Blocks

Interaction Information

Conditional Interaction Information

Normalized Multi-Information

Distribution of Estimated Mutual Information, Multi-information and Interaction Information

Canonical Correlation Analysis Measure for LD between Two Genomic Regions

Association Measure between Two Genomic Regions Based on CCA

Relationship between Canonical Correlation and Joint Information

Software Package

Bibliographical Notes

Appendices

Exercises

Association Studies for Qualitative Traits

Population-based Association Analysis for Common Variants

Introduction

The Hardy-Weinberg Equilibrium

Genetic Models

Odds Ratio

Single Marker Association Analysis

Multi-marker Association Analysis

Population-based Multivariate Association Analysis for Next-generation Sequencing

Multivariate Group Tests

Score Tests and Logistic Regression

Application of Score Tests for Association of Rare Variants

Variance-component Score Statistics and Logistic Mixed Effects Models

Population-based Functional Association Analysis for Next-generation Sequencing

Introduction

Functional Principal Component Analysis for Association Test

Smoothed Functional Principal Component Analysis for Association Test

Software Package

Appendices

Exercises


Association Studies for Quantitative Traits

Fixed Effect Model for a Single Trait

Introduction

Genetic Effects

Linear Regression for a Quantitative Trait

Multiple Linear Regression for a Quantitative Trait

Gene-based Quantitative Trait Analysis

Functional Linear Model for a Quantitative Trait

Canonical Correlation Analysis for Gene-based Quantitative Trait Analysis

Kernel Approach to Gene-based Quantitative Trait Analysis

Kernel and RKHS

Covariance Operator and Dependence Measure

Simulations and Real Data Analysis

Power Evaluation

Application to Real Data Examples

Software Package

Appendices

Exercises


Multiple Phenotype Association Studies

Pleiotropic Additive and Dominance Effects

Multivariate Marginal Regression

Models

Estimation of Genetic Effects

Test Statistics

Linear Models for Multiple Phenotypes and Multiple Markers

Multivariate Multiple Linear Regression Models

Multivariate Functional Linear Models for Gene-based Genetic Analysis of Multiple Phenotypes

Canonical Correlation Analysis for Gene-based Genetic Pleiotropic Analysis

Multivariate Canonical Correlation Analysis (CCA)

Kernel CCA

Functional CCA

Quadratically Regularized Functional CCA

Dependence Measure and Association Tests of Multiple Traits

Principal Component for Phenotype Dimension Reduction

Principal Component Analysis

Kernel Principal Component Analysis

Quadratically Regularized PCA or Kernel PCA

Other Statistics for Pleiotropic Genetics Analysis

Sum of Squared Score Test

Unified Score-based Association Test (USAT)

Combining Marginal Tests

FPCA-based Kernel Measure Test of Independence

Connection between Statistics

Simulations and Real Data Analysis

Type Error Rate and Power Evaluation

Application to Real Data Example

Software Package

Appendices

Exercises


Family-based Association Analysis

Genetic Similarity and Kinship Coefficients

Kinship Coefficients

Identity Coefficients

Relation between identity coefficients and kinship coefficient

Estimation of Genetic Relations from the Data

Genetic Covariance between Relatives

Assumptions and Genetic Models

Analysis for Genetic Covariance between Relatives

Mixed Linear Model for a Single Trait

Genetic Random Effect

Mixed Linear Model for Quantitative Trait Association Analysis

Estimating Variance Components

Hypothesis Test in Mixed Linear Models

Mixed Linear Models for Quantitative Trait Analysis with Sequencing Data

Mixed Functional Linear Models for Sequence-based Quantitative Trait Analysis

Mixed Functional Linear Models (Type )

Mixed Functional Linear Models (Type : Functional Variance Component Models)

Multivariate Mixed Linear Model for Multiple Traits

Multivariate Mixed Linear Model

Maximum Likelihood Estimate of Variance Components

REML Estimate of Variance Components

Heritability

Heritability Estimation for a Single Trait

Heritability Estimation for Multiple Traits





Autore

Momiao Xiong, is a professor in the Department of Biostatistics, University of Texas School of Public Health, and a regular member in the Genetics & Epigenetics (G&E) Graduate Program at The University of Texas MD Anderson Cancer Center, UTHealth Graduate School of Biomedical Science.








Altre Informazioni

ISBN:

9781498725781

Condizione: Nuovo
Collana: Chapman & Hall/CRC Mathematical and Computational Biology
Dimensioni: 10 x 7 in Ø 3.70 lb
Formato: Copertina rigida
Illustration Notes:3 b/w images, 60 color images and 26 tables
Pagine Arabe: 668
Pagine Romane: xxxii





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