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)

Generating code with ChatGPT

ChatGPT is useful for generating R code because it has been trained on a vast amount of programming-related text, including R code examples and best practices. With this knowledge, ChatGPT can understand user queries and provide accurate and contextually relevant R code suggestions. It can generate code snippets for tasks such as data manipulation, visualization, statistical analysis, and package usage. ChatGPT’s ability to leverage its training to generate syntactically correct and efficient R code helps users save time, improve their coding skills, and explore different approaches to problem-solving. It serves as a valuable resource for both novice and experienced R programmers seeking code generation assistance.

In this recipe, we’ll ask ChatGPT to develop a plot for us. Specifically, we’ll be looking for a pairs-style scatter plot of all samples against each other. As we’ll see, it doesn’t get it right but it does...