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)

Clustering with k-means and hierarchical clustering

It is common in bioinformatics to want to classify things into groups without first knowing what or how many groups there may be. This process is usually known as clustering and is a type of unsupervised ML. This is commonly used in genomics experiments, particularly RNAseq and related count-based technologies. In this recipe, we’ll start with a large gene expression dataset with around 150 samples. We’ll learn how to estimate how many groups of samples there are and apply a method to cluster them based on the reduction of dimensionality with PCA followed by a k-means cluster.

Getting ready

We’ll need the factoextra, RColorBrewer, and Bioconductor biobase libraries. We’ll also use the modencodefly_eset object from the rbioinfcookbook package.

How to do it…

We can cluster with the following code

  1. Load the data and run a PCA:
    library(factoextra)library(Biobase)library(rbioinfcookbook...