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R Bioinformatics Cookbook

You're reading from   R Bioinformatics Cookbook Use R and Bioconductor to perform RNAseq, genomics, data visualization, and bioinformatic analysis

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Product type Paperback
Published in Oct 2019
Publisher Packt
ISBN-13 9781789950694
Length 316 pages
Edition 1st Edition
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Authors (2):
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Dr Dan Maclean Dr Dan Maclean
Author Profile Icon Dr Dan Maclean
Dr Dan Maclean
Dan MacLean Dan MacLean
Author Profile Icon Dan MacLean
Dan MacLean
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Toc

Table of Contents (13) Chapters Close

Preface 1. Performing Quantitative RNAseq 2. Finding Genetic Variants with HTS Data FREE CHAPTER 3. Searching Genes and Proteins for Domains and Motifs 4. Phylogenetic Analysis and Visualization 5. Metagenomics 6. Proteomics from Spectrum to Annotation 7. Producing Publication and Web-Ready Visualizations 8. Working with Databases and Remote Data Sources 9. Useful Statistical and Machine Learning Methods 10. Programming with Tidyverse and Bioconductor 11. Building Objects and Packages for Code Reuse 12. Other Books You May Enjoy

Representing relational data as networks

Networks, or graphs, are extremely powerful data representations for relationships between entities that are central to a large number of biological studies. Network analysis can reveal a lot about community structures in ecological studies, reveal potential drug targets in protein-protein interactions, and help us understand the interactions involved in complex metabolic reactions. The underlying data structures that represent networks can be complex. Thankfully, R has got some very powerful packages, in particular, igraph and ggraph, that we can use to access information about our networks and make plots. In this recipe, we'll look at some ways of generating plots for a reasonably sized network.

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