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R Bioinformatics Cookbook - Second Edition

You're reading from R Bioinformatics Cookbook - Second Edition

Product type Book

Published in Oct 2023

Publisher Packt

ISBN-13 9781837634279

Pages 396 pages

Edition 2nd Edition

Languages

Concepts

Author (1):

Dan MacLean

Table of Contents (16) Chapters

Preface

1. Chapter 1: Setting Up Your R Bioinformatics Working Environment

2. Chapter 2: Loading, Tidying, and Cleaning Data in the tidyverse

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

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15. Other Books You May Enjoy

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...

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Authors (1)

Dan MacLean

Professor Dan MacLean has a PhD in molecular biology from the University of Cambridge and gained postdoctoral experience in genomics and bioinformatics at Stanford University in California. Dan is now an honorary professor at the School of Computing Sciences at the University of East Anglia. He has worked in bioinformatics and plant pathogenomics, specializing in R and Bioconductor, and has developed analytical workflows in bioinformatics, genomics, genetics, image analysis, and proteomics at the Sainsbury Laboratory since 2006. Dan has developed and published software packages in R, Ruby, and Python, with over 100,000 downloads combined.

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