Statistical Bioinformatics with R, Second Edition offers a balanced treatment of statistical theory within the context of bioinformatics applications. The book goes beyond gene expression and sequence analysis to include a careful integration of statistical theory in bioinformatics. The inclusion of R codes, along with the development of advanced methodologies such as Bayesian and Markov models, equips students with a solid foundation for conducting bioinformatics research. Sections incorporate the latest advancements in bioinformatics and statistical methodologies, including new chapters on cutting-edge topics such as high-throughput sequencing data analysis, AI/machine learning applications in bioinformatics, and advanced statistical methods.
From new and updated practical examples and case studies that illustrate real-world applications of statistical techniques to bioinformatic problems to enhanced end-of-chapter exercises, detailed code annotations, and an improved companion website with supplementary materials, including datasets and R scripts, this book is a valuable resource for both self-study and formal coursework, fostering a deeper understanding of statistical bioinformatics and equipping readers with the skills needed to tackle complex biological data analysis challenges.
1. Chapter 1. Introduction - p. 1 2. Chapter 2. Fundamentals of Molecular Biology - p. 9 3. Chapter 3. Exploratory Data Analysis - p. 35 4. Abstract - p. 35 5. Contents - p. 35 6. 3.1 Descriptive Statistics for Genomic Data - p. 35 7. 3.2 Theory of Probability - p. 36 8. 3.3 Mathematical or Classical Probability - p. 39 9. 3.4 Sets - p. 42 10. 3.5 Combinatorics - p. 45 11. 3.6 Laws of Probability - p. 48 12. 3.7 Random Variables - p. 52 13. 3.8 Mathematical Expectation - p. 56 14. 3.9 Bivariate Random Variables - p. 60 15. 3.10 Regression - p. 68 16. 3.11 Correlation - p. 74 17. 3.12 Law of Large Numbers and Central Limit Theorem - p. 77 18. 3.13 Visualization Techniques - p. 78 19. 3.14 Dimensionality Reduction - p. 88 20. Chapter 4. Statistical Methods for Bioinformatics - p. 97 21. Abstract - p. 97 22. Contents - p. 97 23. 4.1 Introduction - p. 97 24. 4.2 Estimation - p. 99 25. 4.3 Methods of Estimation - p. 105 26. 4.4 Confidence Intervals - p. 106 27. 4.5 Sample Size - p. 118 28. 4.6 Testing of Hypothesis - p. 119 29. 4.7 Statistical Inference - p. 138 30. 4.8 Multiple Testing of Hypotheses - p. 147 31. Chapter 5. Bayesian Methods in Bioinformatics - p. 161 32. Chapter 6. Artificial Intelligence/Machine Learning in Bioinformatics - p. 193 33. Chapter 7. Nonparametric Statistics - p. 225 34. Chapter 8. Markov Chain Monte Carlo - p. 271 35. Chapter 9. Omics Variance Analysis - p. 303 36. Chapter 10. Optimal Experimental Design in Bioinformatics - p. 341 37. Chapter 11. Proteomics Data Analysis - p. 397 38. Index - p. 417
Statistical Bioinformatics with R, Second Edition offers a balanced treatment of statistical theory within the context of bioinformatics applications. The book goes beyond gene expression and sequence analysis to include a careful integration of statistical theory in bioinformatics. The inclusion of R codes, along with the development of advanced methodologies such as Bayesian and Markov models, equips students with a solid foundation for conducting bioinformatics research. Sections incorporate the latest advancements in bioinformatics and statistical methodologies, including new chapters on cutting-edge topics such as high-throughput sequencing data analysis, AI/machine learning applications in bioinformatics, and advanced statistical methods.
From new and updated practical examples and case studies that illustrate real-world applications of statistical techniques to bioinformatic problems to enhanced end-of-chapter exercises, detailed code annotations, and an improved companion website with supplementary materials, including datasets and R scripts, this book is a valuable resource for both self-study and formal coursework, fostering a deeper understanding of statistical bioinformatics and equipping readers with the skills needed to tackle complex biological data analysis challenges.
1. Chapter 1. Introduction - p. 1 2. Chapter 2. Fundamentals of Molecular Biology - p. 9 3. Chapter 3. Exploratory Data Analysis - p. 35 4. Abstract - p. 35 5. Contents - p. 35 6. 3.1 Descriptive Statistics for Genomic Data - p. 35 7. 3.2 Theory of Probability - p. 36 8. 3.3 Mathematical or Classical Probability - p. 39 9. 3.4 Sets - p. 42 10. 3.5 Combinatorics - p. 45 11. 3.6 Laws of Probability - p. 48 12. 3.7 Random Variables - p. 52 13. 3.8 Mathematical Expectation - p. 56 14. 3.9 Bivariate Random Variables - p. 60 15. 3.10 Regression - p. 68 16. 3.11 Correlation - p. 74 17. 3.12 Law of Large Numbers and Central Limit Theorem - p. 77 18. 3.13 Visualization Techniques - p. 78 19. 3.14 Dimensionality Reduction - p. 88 20. Chapter 4. Statistical Methods for Bioinformatics - p. 97 21. Abstract - p. 97 22. Contents - p. 97 23. 4.1 Introduction - p. 97 24. 4.2 Estimation - p. 99 25. 4.3 Methods of Estimation - p. 105 26. 4.4 Confidence Intervals - p. 106 27. 4.5 Sample Size - p. 118 28. 4.6 Testing of Hypothesis - p. 119 29. 4.7 Statistical Inference - p. 138 30. 4.8 Multiple Testing of Hypotheses - p. 147 31. Chapter 5. Bayesian Methods in Bioinformatics - p. 161 32. Chapter 6. Artificial Intelligence/Machine Learning in Bioinformatics - p. 193 33. Chapter 7. Nonparametric Statistics - p. 225 34. Chapter 8. Markov Chain Monte Carlo - p. 271 35. Chapter 9. Omics Variance Analysis - p. 303 36. Chapter 10. Optimal Experimental Design in Bioinformatics - p. 341 37. Chapter 11. Proteomics Data Analysis - p. 397 38. Index - p. 417
