Book Image

R Bioinformatics Cookbook - Second Edition

By : Dan MacLean
Book Image

R Bioinformatics Cookbook - Second Edition

By: Dan MacLean

Overview of this book

The updated second edition of R Bioinformatics Cookbook takes a recipe-based approach to show you how to conduct practical research and analysis in computational biology with R. You’ll learn how to create a useful and modular R working environment, along with loading, cleaning, and analyzing data using the most up-to-date Bioconductor, ggplot2, and tidyverse tools. This book will walk you through the Bioconductor tools necessary for you to understand and carry out protocols in RNA-seq and ChIP-seq, phylogenetics, genomics, gene search, gene annotation, statistical analysis, and sequence analysis. As you advance, you'll find out how to use Quarto to create data-rich reports, presentations, and websites, as well as get a clear understanding of how machine learning techniques can be applied in the bioinformatics domain. The concluding chapters will help you develop proficiency in key skills, such as gene annotation analysis and functional programming in purrr and base R. Finally, you'll discover how to use the latest AI tools, including ChatGPT, to generate, edit, and understand R code and draft workflows for complex analyses. By the end of this book, you'll have gained a solid understanding of the skills and techniques needed to become a bioinformatics specialist and efficiently work with large and complex bioinformatics datasets.
Table of Contents (16 chapters)

Testing and accounting for interactions between variables in linear models

An interaction between variables occurs when the effect of one predictor variable on the response variable depends on the level of another predictor variable. In other words, the effect of one variable is not constant across different levels of the other variable. The interaction can occur between different drug regimes in medical trials or generally multiple experimental conditions being changed.

Linear models can model interactions by including interaction terms in the model formula. An interaction term is the product of two or more predictor variables, where each predictor variable is centered to have a mean of zero.

Suppose we have a linear regression model with two predictor variables, x1 and x2, and we want to examine their interaction. The interaction term can be included in the model as follows:

y = β 0 + β 1 x 1 + β 2 x 2 + β 3(x 1...