Modern Statistics for Modern Biology

Erscheinungsjahr 2019
ISBN: 978-1-108-70529-5
Verlag: Cambridge University Press

Buch, Englisch, 454 Seiten, Format (B × H): 220 mm x 280 mm, Gewicht: 1128 g

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.

Autoren/Hrsg.

Holmes, Susan

Huber, Wolfgang

Fachgebiete

• Mathematik | Informatik Mathematik Stochastik Mathematische Statistik
• Naturwissenschaften Biowissenschaften Biowissenschaften
• Technische Wissenschaften Verfahrenstechnik | Chemieingenieurwesen | Biotechnologie Biotechnologie
• Mathematik | Informatik EDV | Informatik Daten / Datenbanken Automatische Datenerfassung, Datenanalyse
• Naturwissenschaften Biowissenschaften Angewandte Biologie Biomathematik
• Interdisziplinäres Wissenschaften Wissenschaften Interdisziplinär Mathematik für Naturwissenschaftler
• Mathematik | Informatik EDV | Informatik Daten / Datenbanken Data Mining

Inhaltsverzeichnis

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.

Über Autor(innen)

Holmes, Susan
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.

Huber, Wolfgang
Wolfgang Huber is Research Group Leader and Senior Scientist at the European Molecular Biological Laboratory, where he develops computational methods for new biotechnologies and applies them to biological discovery. He has published over 150 research papers in functional genomics, cancer and statistical methods. He is a founding member of the open-source bioinformatics software collaboration Bioconductor and has co-authored two books on Bioconductor.