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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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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

Quantifying and estimating the differences between trees with treespace

Comparing trees to differentiate or group them can help researchers to see patterns of evolution. Multiple trees of a single gene tracked across species or strains can reveal differences in how that gene is changing across species. At the core of these approaches are metrics of distances between trees. In this recipe, we’ll calculate one such metric to find pairwise differences between 20 different genes in 15 different species, hence 15 different tips with identical names in each tree. Such similarity in trees is needed to compare and get distances, and we can’t do an analysis like this unless these conditions are met.

Getting ready

For this recipe, we’ll use the treespace package to compute distances and clusters. We’ll use ape and adegraphics for accessory loading and visualization functions. The input data will be 20 files of Newick format trees, each of which represents a...

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