Statistical Bioinformatics: For Biomedical And Life Science Researchers

This book provides an essential understanding of statistical concepts necessary for the analysis of genomic and proteomic data using computational techniques. The author presents both basic and advanced topics, focusing on those that are relevant to the computational analysis of large data sets in biology. Chapters begin with a description of a statistical concept and a current example from biomedical research, followed by more detailed presentation, discussion of limitations, and problems. The book starts with an introduction to probability and statistics for genome-wide data, and moves into topics such as clustering, classification, multi-dimensional visualization, experimental design, statistical resampling, and statistical network analysis.


* Clearly explains the use of bioinformatics tools in life sciences research without requiring an advanced background in math/statistics

* Enables biomedical and life sciences researchers to successfully evaluate the validity of their results and make inferences

* Enables statistical and quantitative researchers to rapidly learn novel statistical concepts and techniques appropriate for large biological data analysis

* Carefully revisits frequently used statistical approaches and highlights their limitations in large biological data analysis

* Offers programming examples and datasets

* Includes chapter problem sets, a glossary, a list of statistical notations, and appendices with references to background mathematical and technical material

* Features supplementary materials, including datasets, links, and a statistical package available online



Statistical Bioinformatics is an ideal textbook for students in medicine, life sciences, and bioengineering, aimed at researchers who utilize computational tools for the analysis of genomic, proteomic, and many other emerging high-throughput molecular data. It may also serve as a rapid introduction to the bioinformatics science for statistical and computational students and audiences who have not experienced such analysis tasks before.

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Written for biologists rather than statisticians or computer scientists.

Jae K. Lee, Ph.D., is a professor of biostatistics and epidemiology in the Department of Health Evaluation Sciences at the University of Virginia School of Medicine, where he designed and teaches a course on Statistical Bioinformatics in Medicine. He earned his doctorate in statistical genetics from the University of Wisconsin, Madison. He was previously a research scientist in the Laboratory of Molecular Pharmacology, National Cancer Institute. Among his current research interests is the integration of statistical and genomic information for the analysis of microarray data.

PREFACE xi


CONTRIBUTORS xiii


1 ROAD TO STATISTICAL BIOINFORMATICS 1


Challenge 1: Multiple-Comparisons Issue 1


Challenge 2: High-Dimensional Biological Data 2


Challenge 3: Small-n and Large-p Problem 3


Challenge 4: Noisy High-Throughput Biological Data 3


Challenge 5: Integration of Multiple, Heterogeneous Biological Data Information 3


References 5


2 PROBABILITY CONCEPTS AND DISTRIBUTIONS FOR ANALYZING LARGE BIOLOGICAL DATA 7


2.1 Introduction 7


2.2 Basic Concepts 8


2.3 Conditional Probability and Independence 10


2.4 Random Variables 13


2.5 Expected Value and Variance 15


2.6 Distributions of Random Variables 19


2.7 Joint and Marginal Distribution 39


2.8 Multivariate Distribution 42


2.9 Sampling Distribution 46


2.10 Summary 54


3 QUALITY CONTROL OF HIGH-THROUGHPUT BIOLOGICAL DATA 57


3.1 Sources of Error in High-Throughput Biological Experiments 57


3.2 Statistical Techniques for Quality Control 59


3.3 Issues Specific to Microarray Gene Expression Experiments 66


3.4 Conclusion 69


References 69


4 STATISTICAL TESTING AND SIGNIFICANCE FOR LARGE BIOLOGICAL DATA ANALYSIS 71


4.1 Introduction 71


4.2 Statistical Testing 72


4.3 Error Controlling 78


4.4 Real Data Analysis 81


4.5 Concluding Remarks 87


Acknowledgments 87


References 88


5 CLUSTERING: UNSUPERVISED LEARNING IN LARGE BIOLOGICAL DATA 89


5.1 Measures of Similarity 90


5.2 Clustering 99


5.3 Assessment of Cluster Quality 115


5.4 Conclusion 123


References 123


6 CLASSIFICATION: SUPERVISED LEARNING WITH HIGH-DIMENSIONAL BIOLOGICAL DATA 129


6.1 Introduction 129


6.2 Classification and Prediction Methods 132


6.3 Feature Selection and Ranking 140


6.4 Cross-Validation 144


6.5 Enhancement of Class Prediction by Ensemble Voting Methods 145


6.6 Comparison of Classification Methods Using High-Dimensional Data 147


6.7 Software Examples for Classification Methods 150


References 154


7 MULTIDIMENSIONAL ANALYSIS AND VISUALIZATION ON LARGE BIOMEDICAL DATA 157


7.1 Introduction 157


7.2 Classical Multidimensional Visualization Techniques 158


7.3 Two-Dimensional Projections 161


7.4 Issues and Challenges 165


7.5 Systematic Exploration of Low-Dimensional Projections 166


7.6 One-Dimensional Histogram Ordering 170


7.7 Two-Dimensional Scatterplot Ordering 174


7.8 Conclusion 181


References 182


8 STATISTICAL MODELS, INFERENCE, AND ALGORITHMS FOR LARGE BIOLOGICAL DATA ANALYSIS 185


8.1 Introduction 185


8.2 Statistical/Probabilistic Models 187


8.3 Estimation Methods 189


8.4 Numerical Algorithms 191


8.5 Examples 192


8.6 Conclusion 198


References 199


9 EXPERIMENTAL DESIGNS ON HIGH-THROUGHPUT BIOLOGICAL EXPERIMENTS 201


9.1 Randomization 201


9.2 Replication 202


9.3 Pooling 209


9.4 Blocking 210


9.5 Design for Classifications 214


9.6 Design for Time Course Experiments 215


9.7 Design for eQTL Studies 215


References 216


10 STATISTICAL RESAMPLING TECHNIQUES FOR LARGE BIOLOGICAL DATA ANALYSIS 219


10.1 Introduction 219


10.2 Resampling Methods for Prediction Error Assessment and Model Selection 221


10.3 Feature Selection 225


10.4 Resampling-Based Classification Algorithms 226


10.5 Practical Example: Lymphoma 226


10.6 Resampling Methods 227


10.7 Bootstrap Methods 232


10.8 Sample Size Issues 233


10.9 Loss Functions 235


10.10 Bootstrap Resampling for Quantifying Uncertainty 236


10.11 Markov Chain Monte Carlo Methods 238


10.12 Conclusions 240


References 247


11 STATISTICAL NETWORK ANALYSIS FOR BIOLOGICAL SYSTEMS AND PATHWAYS 249


11.1 Introduction 249


11.2 Boolean Network Modeling 250


11.3 Bayesian Belief Network 259


11.4 Modeling of Metabolic Networks 273


References 279


12 TRENDS AND STATISTICAL CHALLENGES IN GENOMEWIDE ASSOCIATION STUDIES 283


12.1 Introduction 283


12.2 Alleles, Linkage Disequilibrium, and Haplotype 283


12.3 International HapMap Project 285


12.4 Genotyping Platforms 286


12.5 Overview of Current GWAS Results 287


12.6 Statistical Issues in GWAS 290


12.7 Haplotype Analysis 296


12.8 Homozygosity and Admixture Mapping 298


12.9 Gene Gene and Gene Environment Interactions 298


12.10 Gene and Pathway-Based Analysis 299


12.11 Disease Risk Estimates 301


12.12 Meta-Analysis 301


12.13 Rare Variants and Sequence-Based Analysis 302


12.14 Conclusions 302


Acknowledgments 303


References 303


13 R AND BIOCONDUCTOR PACKAGES IN BIOINFORMATICS: TOWARDS SYSTEMS BIOLOGY 309


13.1 Introduction 309


13.2 Brief overview of the Bioconductor Project 310


13.3 Experimental Data 311


13.4 Annotation 318


13.5 Models of Biological Systems 328


13.6 Conclusion 335


13.7 Acknowledgments 336


References 336


INDEX 339