Statistical bioinformatics : a guide for life and biomedical science researchers / edited by Jae K. Lee. — Oxford : Wiley-Blackwell, c2010. – (58.17115/S797)

Contents

    CONTENTS
    
    PREFACE
    CONTR1B UTORS
    1 ROAD TO STATISTICAL BIOINFORMATICS
    Challenge 1: Multiple-Comparisons Issue
    Challenge 2: High-Dimensional Biological Data
    Challenge 3: Small-n and Large-p Problem
    Challenge 4: Noisy High-Throughput Biological Data
    Challenge 5: Integration of Multiple， Heterogeneous Biological Data Information
    References
    2 PROBABILITY CONCEPTS AND DISTRIBUTIONS FOR ANALYZING LARGE BIOLOGICAL DATA
    2.1 Introduction
    2.2 Basic Concepts
    2.3 Conditional Probability and Independence
    2.4 Random Variables
    2.5 Expected Value and Variance
    2.6 Distributions of Random Variables
    2.7 Joint and Marginal Distribution
    2.8 Multivariate Distribution
    2.9 Sampling Distribution
    2.10 Summary
    3 QUALITY CONTROL OF HIGH-THROUGHPUT BIOLOGICAL DATA
    3.1 Sources of Error in High-Throughput Biological Experiments
    3.2 Statistical Techniques for Quality Control
    3.3 Issues Specific to Microarray Gene Expression Experiments
    3.4 Conclusion
    References
    4 STATISTICAL TESTING AND SIGNIFICANCE FOR LARGE BIOLOGICAL DATA ANALYSIS
    4.l Introduction
    4.2 Statistical Testing
    4.3 Error Controlling
    4.4 Real Data Analysis
    4.5 Concluding Remarks
    Acknowledgments
    References
    5 CLUSTERING: UNSUPERVISED LEARNING IN LARGE BIOLOGICAL DATA
    5.1 Measures of Similarity
    5.2 Clustering
    5.3 Assessment of Cluster Quality
    5.4 Conclusion
    References
    6 CLASSIFICATION: SUPERVISED LEARNING WITH HIGH-DIMENSIONAL BIOLOGICAL DATA
    6.1 Introduction
    6.2 Classification and Prediction Methods
    6.3 Feature Selection and Ranking
    6.4 Cross-Validation
    6.5 Enhancement of Class Prediction by Ensemble Voting Methods
    6.6 Comparison of Classification Methods Using High-Dimensional Data
    6.7 Software Examples for Classification Methods
    References
    7 MULTIDIMENSIONAL ANALYSIS AND VISUALIZATION ON LARGE BIOMEDICAL DATA
    7.1 Introduction
    7.2 Classical Multidimensional Visualization Techniques
    7.3 Two-Dimensional Projections
    7.4 Issues and Challenges
    7.5 Systematic Exploration of Low-Dimensional Projections
    7.6 One-Dimensional Histogram Ordering
    7.7 Two-Dimensional Scatterplot Ordering
    7.8 Conclusion
    References
    8 STATISTICAL MODELS， INFERENCE， AND ALGORITHMS FOR LARGE BIOLOGICAL DATA ANALYSIS
    8.1 Introduction
    8.2 Statistical/Probabilistic Models
    8.3 Estimation Methods
    8.4 Numerical Algorithms
    8.5 Examples
    8.6 Conclusion
    References
    9 EXPERIMENTAL DESIGNS ON HIGH-THROUGHPUT BIOLOGICAL EXPERIMENTS
    9.1 Randomization
    9.2 Replication
    9.3 Pooling
    9.4 Blocking
    9.5 Design for Classifications
    9.6 Design for Time Course Experiments
    9.7 Design for eQTL Studies
    References
    10 STATISTICAL RESAMPLING TECHNIQUES FOR LARGE BIOLOGICAL DATA ANALYSIS
    10.1 Introduction
    10.2 Resampling Methods for Prediction Error Assessment and Model Selection
    10.3 Feature Selection
    10.4 Resampling-Based Classification Algorithms
    10.5 Practical Example: Lymphoma
    10.6 Resampling Methods
    10.7 Bootstrap Methods
    10.8 Sample Size Issues
    10.9 Loss Functions
    10.10 Bootstrap Resampling for Quantifying Uncertainty
    10.11 Markov Chain Monte Carlo Methods
    10.12 Conclusions
    References
    11 STATISTICAL NETWORK ANALYSIS FOR BIOLOGICAL SYSTEMS AND PATHWAYS
    11.1 Introduction
    11.2 Boolean Network Modeling
    11.3 Bayesian Belief Network
    11.4 Modeling of Metabolic Networks
    References
    12 TRENDS AND STATISTICAL CHALLENGES IN GENOMEW1DE ASSOCIATION STUDIES
    12.1 Introduction
    12.2 Alleles， Linkage Disequilibrium， and Haplotype
    12.3 International HapMap Project
    12.4 Genotyping Platforms
    12.5 Overview of Current GWAS Results
    12.6 Statistical Issues in GWAS
    12.7 Haplotype Analysis
    12.8 Homozygosity and Admixture Mapping
    12.9 Gene Gene and Gene x Environment Interactions
    12.10 Gene and Pathway-Based Analysis
    12.11 Disease Risk Estimates
    12.12 Meta-Analysis
    12.13 Rare Variants and Sequence-Based Analysis
    12.14 Conclusions
    Acknowledgments
    References
    13 R AND BIOCONDUCTOR PACKAGES IN BIOINFORMATICS: TOWARDS SYSTEMS BIOLOGY
    13.1 Introduction
    13.2 Brief overview of the Bioconductor Project
    13.3 Experimental Data
    13.4 Annotation
    13.5 Models of Biological Systems
    13.6 Conclusion
    13.7 Acknowledgments
    References
    INDEX