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Book

Data analysis for the life sciences with R

Milton : CRC Press, 2016

376 stran

Cover -- Half Title -- Title Page -- Copyright Page -- Contents -- List of Figures -- Acknowledgments -- Introduction -- 1. Getting Started -- 1.1. Installing R -- 1.2. Installing RStudio -- 1.3. Learn R Basics -- 1.4. Installing Packages -- 1.5. Importing Data into R -- 1.6. Exercises -- 1.7. Brief Introduction to dplyr -- 1.8. Exercises -- 1.9. Mathematical Notation -- 2. Inference -- 2.1. Introduction -- 2.2. Random Variables -- 2.3. The Null Hypothesis -- 2.4. Distributions -- 2.5. Probability Distribution -- 2.6. Normal Distribution -- 2.7. Exercises

2.8. Populations, Samples and Estimates -- 2.9. Exercises -- 2.10. Central Limit Theorem and t-distribution -- 2.11. Exercises -- 2.12. Central Limit Theorem in Practice -- 2.13. Exercises -- 2.14. t-tests in Practice -- 2.15. The t-distribution in Practice -- 2.16. Confidence Intervals -- 2.17. Power Calculations -- 2.18. Exercises -- 2.19. Monte Carlo Simulation -- 2.20. Parametric Simulations for the Observations -- 2.21. Exercises -- 2.22 Permutation Tests -- 2.23. Exercises -- 2.24. Association Tests -- 2.25. Exercises -- 3. Exploratory Data Analysis -- 3.1. Quantile Quantile Plots

3.2. Boxplots -- 3.3. Scatterplots and Correlation -- 3.4. Stratification -- 3.5. Bivariate Normal Distribution -- 3.6. Plots to Avoid -- 3.7. Misunderstanding Correlation (Advanced) -- 3.8. Exercises -- 3.9. Robust Summaries -- 3.10. Wilcoxon Rank Sum Test -- 3.11. Exercises -- 4. Matrix Algebra -- 4.1. Motivating Examples -- 4.2. Exercises -- 4.3. Matrix Notation -- 4.4. Solving Systems of Equations -- 4.5. Vectors, Matrices, and Scalars -- 4.6. Exercises -- 4.7. Matrix Operations -- 4.8. Exercises -- 4.9. Examples -- 4.10. Exercises -- 5. Linear Models -- 5.1. Exercises

5.2. The Design Matrix -- 5.3. Exercises -- 5.4. The Mathematics Behind lm() -- 5.5. Exercises -- 5.6. Standard Errors -- 5.7. Exercises -- 5.8. Interactions and Contrasts -- 5.9. Linear Model with Interactions -- 5.10. Analysis of Variance -- 5.11. Exercises -- 5.12. Collinearity -- 5.13. Rank -- 5.14. Removing Confounding -- 5.15. Exercises -- 5.16. The QR Factorization (Advanced) -- 5.17. Going Further -- 6. Inference for High Dimensional Data -- 6.1. Introduction -- 6.2. Exercises -- 6.3. Inference in Practice -- 6.4. Exercises -- 6.5. Procedures -- 6.6. Error Rates

6.7. The Bonferroni Correction -- 6.8. False Discovery Rate -- 6.9. Direct Approach to FDR and q-values (Advanced) -- 6.10. Exercises -- 6.11. Basic Exploratory Data Analysis -- 6.12. Exercises -- 7. Statistical Models -- 7.1. The Binomial Distribution -- 7.2. The Poisson Distribution -- 7.3. Maximum Likelihood Estimation -- 7.4. Distributions for Positive Continuous Values -- 7.5. Exercises -- 7.6. Bayesian Statistics -- 7.7. Exercises -- 7.8. Hierarchical Models -- 7.9. Exercises -- 8. Distance and Dimension Reduction -- 8.1. Introduction -- 8.2. Euclidean Distance

8.3. Distance in High Dimensions

This book covers several of the statistical concepts and data analytic skills needed to succeed in data-driven life science research. The authors proceed from relatively basic concepts related to computed p-values to advanced topics related to analyzing highthroughput data. They include the R code that performs this analysis and connect the lines of code to the statistical and mathematical concepts explained. Nakladatelská anotace