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R Bioinformatics Cookbook

You're reading from   R Bioinformatics Cookbook Utilize R packages for bioinformatics, genomics, data science, and machine learning

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Product type Paperback
Published in Oct 2023
Publisher Packt
ISBN-13 9781837634279
Length 396 pages
Edition 2nd Edition
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Author (1):
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Dan MacLean Dan MacLean
Author Profile Icon Dan MacLean
Dan MacLean
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Toc

Table of Contents (16) Chapters Close

Preface 1. Chapter 1: Setting Up Your R Bioinformatics Working Environment 2. Chapter 2: Loading, Tidying, and Cleaning Data in the tidyverse FREE CHAPTER 3. Chapter 3: ggplot2 and Extensions for Publication Quality Plots 4. Chapter 4: Using Quarto to Make Data-Rich Reports, Presentations, and Websites 5. Chapter 5: Easily Performing Statistical Tests Using Linear Models 6. Chapter 6: Performing Quantitative RNA-seq 7. Chapter 7: Finding Genetic Variants with HTS Data 8. Chapter 8: Searching Gene and Protein Sequences for Domains and Motifs 9. Chapter 9: Phylogenetic Analysis and Visualization 10. Chapter 10: Analyzing Gene Annotations 11. Chapter 11: Machine Learning with mlr3 12. Chapter 12: Functional Programming with purrr and base R 13. Chapter 13: Turbo-Charging Development in R with ChatGPT 14. Index 15. Other Books You May Enjoy

Highlighting selected values in busy plots with gghighlight

Bioinformatics datasets often comprise measurements of many items. The genomes we analyze have thousands of genes, but usually, we’re only interested in the few that respond to particular changes in the experiment we have designed. So, it’s of great use to be able to highlight those few in our plots. In this recipe, we’ll look at the gghighlight package, which can make that very easy.

Getting ready

We’ll need the gghighlight, ggplot2, and rbioinfcookbook packages for the main functions. We’ll also use dplyr briefly. The datasets for these are fission yeast wt versus mutant gene expression data and an Arabidopsis treatment timecourse. The columns in the data are for the log 2 ratio of gene expression in mutant versus wt and the p-value from a statistical test.

How to do it…

We can highlight selected values in a plot such as a gene expression plot using the following steps...

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