Susan Holmes is Professor of Statistics at Stanford University, California. She specializes in exploring and visualizing multidomain biological data, using computational statistics to draw inferences in microbiology, immunology and cancer biology. She has published over 100 research papers, and has been a key developer of software for the multivariate analyses of complex heterogeneous data. She was the Breiman Lecturer at NIPS 2016, has been named a Fields Institute fellow, and is currently a fellow at the Center for the Advances Study of the Behavioral Sciences.

Résumé
Designed for a new generation of biologists, this textbook teaches modern computational statistics by using R/Bioconductor to analyze experimental data from high-throughput technologies. The presentation minimizes mathematical notation and emphasizes inductive understanding from well-chosen examples, hands-on simulation, and visualization.

Contenu

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.