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)

Using the renv package to create a project-specific set of packages

In the context of software development, an environment refers to a specific set of dependencies and software versions that is used to run a particular application or project. They are useful for avoiding conflicts between different projects that may require different versions of the same package, and for ensuring that the application runs consistently across different development, test, and production environments. The renv R package is a dependency management tool for R projects. It allows the user to create a snapshot of the packages and versions used in a project so that others can easily reproduce the same environment. It also helps in keeping the package’s version consistent across different machines and different team members working on the same project. It can help to avoid package version conflicts and can also help in maintaining the reproducibility of the project results. It is particularly useful...