Book Image

R Bioinformatics Cookbook

By : Dan MacLean
Book Image

R Bioinformatics Cookbook

By: Dan MacLean

Overview of this book

Handling biological data effectively requires an in-depth knowledge of machine learning techniques and computational skills, along with an understanding of how to use tools such as edgeR and DESeq. With the R Bioinformatics Cookbook, you’ll explore all this and more, tackling common and not-so-common challenges in the bioinformatics domain using real-world examples. This book will use a recipe-based approach to show you how to perform practical research and analysis in computational biology with R. You will learn how to effectively analyze your data with the latest tools in Bioconductor, ggplot, and tidyverse. The book will guide you through the essential tools in Bioconductor to help you understand and carry out protocols in RNAseq, phylogenetics, genomics, and sequence analysis. As you progress, you will get up to speed with how machine learning techniques can be used in the bioinformatics domain. You will gradually develop key computational skills such as creating reusable workflows in R Markdown and packages for code reuse. By the end of this book, you’ll have gained a solid understanding of the most important and widely used techniques in bioinformatic analysis and the tools you need to work with real biological data.
Table of Contents (13 chapters)

Building Objects and Packages for Code Reuse

In this final chapter, we'll take a look at taking our code out of our own machines and sharing it with the world. The person we'll share with most often will be ourselves! So, with a view to making our own programming lives easier and more streamlined, we'll look at how to create objects and classes to simplify our own workflows and how to bundle them into packages for reuse in other projects. We'll look at tools for sharing code on sites such as GitHub and how to check that everything in your code works the way it is supposed to.

The following recipes will be covered in this chapter:

  • Creating simple S3 objects to simplify code
  • Taking advantage of generic object functions with S3 classes
  • Creating structured and formal objects with the S4 system
  • Simple ways to package code for sharing and reuse
  • Using devtools to...