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)

Functional Programming with purrr and base R

Functional programming is a programming paradigm that emphasizes immutability, pure functions, and declarative programming, treating computation as the evaluation of mathematical functions and avoiding shared state and side effects. It focuses on composing functions and working with immutable data to create robust and predictable programs. In terms of our day-to-day work, this means focusing on using functions to solve problems and avoiding changing data. We also emphasize writing clear, reusable code that is easier to understand and predictable in its behavior. This style helps us as programmers, especially when code becomes complex or must be maintained over a longer period of time.

R has several base functions, including the apply family, as well as external packages that support functional programming, including purrr, and tidyverse. These tool kits provide functions and operators that facilitate working with data using a functional...