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)

Visualizing multiple distributions with ridgeplots

Visualizing distributions of some measured quantity is an extremely common task in bioinformatics, and one that base R handles admirably with its hist() and density() functions and the generic plot() methods, which can create plots of the objects. The ggplot graphics system has a neat way of plotting many density graphs in a per factor level manner, resulting in a compact and very readable graphic—a so-called ridgeplot. In this recipe, we'll look at how to create a ridgeplot.

Getting ready

In this recipe, we'll use the ggplot and ggridges packages. For the dataset, we'll use one from the datasets package that usually comes preinstalled with R. We&apos...