Modern statistics for modern biology

Table of Contents
Introduction
1. Generative models for discrete data
2. Statistical modeling
3. High-quality graphics in R
4. Mixture models
5. Clustering
6. Testing
7. Multivariate analysis
8. High-throughput count data
9. Multivariate methods for heterogeneous data
10. Networks and trees
11. Image data
12. Supervised learning
13. Design of high-throughput experiments and their analyses
Statistical concordance
Bibliography
Index.

If you are a biologist and want to get the best out of the powerful methods of modern computational statistics, this is your book. You can visualize and analyze your own data, apply unsupervised and supervised learning, integrate datasets, apply hypothesis testing, and make publication-quality figures using the power of R/Bioconductor and ggplot2. This book will teach you 'cooking from scratch', from raw data to beautiful illuminating output, as you learn to write your own scripts in the R language and to use advanced statistics packages from CRAN and Bioconductor. It covers a broad range of basic and advanced topics important in the analysis of high-throughput biological data, including principal component analysis and multidimensional scaling, clustering, multiple testing, unsupervised and supervised learning, resampling, the pitfalls of experimental design, and power simulations using Monte Carlo, and it even reaches networks, trees, spatial statistics, image data, and microbial ecology. Using a minimum of mathematical notation, it builds understanding from well-chosen examples, simulation, visualization, and above all hands-on interaction with data and code.

https://www.cambridge.org/gb/academic/subjects/statistics-probability/statistics-life-sciences-medicine-and-health/modern-statistics-modern-biology?format=PB&isbn=9781108705295