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)

Making interactive plots with plotly

Interactive plots are great tools for data exploration, allowing users to explore interactively large datasets to gain insights and identify patterns in data. They are useful for programmers wishing to create dashboards for visualizing real-time data and help with interactive presentations that can communicate complex data relationships in an engaging manner. plotly is a data visualization library for creating interactive plots in Python, R, and JavaScript. It provides a high-level interface for drawing attractive and informative statistical graphics, and the ggplotly package in R allows you to convert static ggplot2 visualizations to interactive plots through a high-level interface. In this recipe, we’ll create a fairly involved ggplot2 visualization of mutation sites on a genome and then convert it to plotly to get a great first-level interaction layer.

Getting ready

We’ll need the ggplot2, plotly, and rbioinfcookbook packages...