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)

Clarifying label placement with ggrepel

Bioinformatics datasets often have many thousands of data points. These can be genomic positions or genes within a genome, and as part of our data analysis, we will frequently want to label positions or genes so that the reader can identify them. A problem arises in that the labels can easily overlap or clash in the plots. The ggrepel package provides geoms for ggplot2 that allow for labels to be positioned much more clearly, incorporating label layout algorithms that make labels and connecting lines repel intelligently. In this recipe, we’ll look at the most important options for applying that to a genomics dataset.

Getting ready

We’ll need the ggplot2 and ggrepel packages and the fission yeast gene expression dataset in the rbioinfcookbook data package. This data frame contains yeast gene IDs in one column, the log 2-fold change of gene expression for that gene, and the p-value from a statistical test.

How to do it…...