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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
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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

Technical requirements

The sample data you'll need is available from this book's GitHub repository: https://github.com/PacktPublishing/R-Bioinformatics_Cookbook. If you want to use the code examples as they are written, then you will need to make sure that this data is in a sub-directory of whatever your working directory is.

Here are the R packages that you'll need. Most of these will install with install.packages(); others are a little more complicated:

  • Bioconductor
    • AllelicImbalance
    • bumphunter
    • csaw
    • DESeq
    • edgeR
    • IRanges
    • Rsamtools
    • rtracklayer
    • sva
    • SummarizedExperiment
    • VariantAnnotation
  • dplyr
  • extRemes
  • forcats
  • magrittr
  • powsimR
  • readr

Bioconductor is huge and has its own installation manager. You can install it with the following code:

if (!requireNamespace("BiocManager"))
    install.packages("BiocManager")
BiocManager::install()
Further information is available at https://www.bioconductor.org/install/.

Normally, in R, a user will load a library and use the functions directly by name. This is great in interactive sessions but it can cause confusion when many packages are loaded. To clarify which package and function I'm using at a given moment, I will occasionally use the packageName::functionName() convention.

Sometimes, in the middle of a recipe, I'll interrupt the code so you can see some intermediate output or the structure of an object it's important to understand. Whenever that happens, you'll see a code block where each line begins with ## (double hash symbols). Consider the following command:

letters[1:5]

This will give us output as follows:

## a b c d e

Note that the output lines are prefixed with ##.

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