Modern Statistics for Modern Biology

This book is a comprehensive guide for biologists who want to learn how to use modern computational statistics to analyze high-throughput biological data. It covers a wide range of topics, from basic to advanced, and teaches readers how to write their own scripts in R and use advanced statistics packages from CRAN and Bioconductor.

Format: Paperback / softback
Length: 402 pages
Publication date: 28 February 2019
Publisher: Cambridge University Press

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.

Weight: 1138g
Dimension: 279 x 218 x 16 (mm)
ISBN-13: 9781108705295