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)

Using ClusterProfiler for determining GO enrichment in clusters

GO analysis involves the use of ontologies to annotate genes based on their biological function, cellular component, and molecular processes. The GO Consortium provides a controlled vocabulary of terms that describe gene function, and these terms are arranged in a hierarchical structure. It aids in the interpretation of high-throughput genomic data, such as microarray and RNA-seq data, by identifying enriched biological themes and pathways among the differentially expressed genes.

In Bioconductor, GO analysis can be performed using various packages such as org.Hs.eg.db, GOstats, and clusterProfiler. These packages allow the user to map gene identifiers to GO terms and perform statistical tests to identify enriched terms in a set of genes.

In this recipe, we will look at how to go from a set of genes in a generic input to assessing them with plots from different GO-related packages.

Getting ready

For this recipe...