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)

Highlighting selected values in busy plots with gghighlight

Bioinformatics datasets often comprise measurements of many items. The genomes we analyze have thousands of genes, but usually, we’re only interested in the few that respond to particular changes in the experiment we have designed. So, it’s of great use to be able to highlight those few in our plots. In this recipe, we’ll look at the gghighlight package, which can make that very easy.

Getting ready

We’ll need the gghighlight, ggplot2, and rbioinfcookbook packages for the main functions. We’ll also use dplyr briefly. The datasets for these are fission yeast wt versus mutant gene expression data and an Arabidopsis treatment timecourse. The columns in the data are for the log 2 ratio of gene expression in mutant versus wt and the p-value from a statistical test.

How to do it…

We can highlight selected values in a plot such as a gene expression plot using the following steps...