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)

Zooming and making callouts from selected plot sections with facetzoom

We’ve already seen in these recipes how bioinformatics datasets can encompass very large scales. Genomes can be thousands of millions of bases long and contain tens of thousands of genes, taxa can have thousands of members, and biomes can have billions of individuals living in areas of a wide range of sizes. Contextual information is therefore often important in analysis and visualization; we may want to see a detail of some subset of data in its original broader context. We can do that by using plots with callout-style subplots—zoomed-in areas drawn alongside the wider data. In this recipe, we will look at using the facet zoom functionality in the ggforce package to look at an area of interest in a ggplot.

Getting ready

We’ll use the ggplot2, ggforce, palmerpenguins, and rbioinfcookbook packages for the main part of this recipe. The allele_freq and penguins datasets will be the basis...