R Bioinformatics Cookbook : Utilize R Packages for Bioinformatics, Genomics, Data Science, and Machine Learning / Dan MacLean.

Format:

Book

Author/Creator:

Maclean, Dan, author.

Language:

English

Subjects (All):

Bioinformatics.

R (Computer program language).

Computational biology.

Physical Description:

1 online resource (396 pages)

Edition:

Second edition.

Place of Publication:

Birmingham, England : Packt Publishing Ltd., [2023]

Summary:

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.

Contents:

Cover

Title Page

Copyright and Credits

Dedications

Contributors

Table of Contents

Preface

Chapter 1: Setting Up Your R Bioinformatics Working Environment

Technical requirements

Further information

Setting up an R project in a directory

Getting ready

How to do it…

How it works…

There's more…

Using the here package to simplify working with paths

Using the devtools package to work with the latest non-CRAN packages

Setting up your machine for the compilation of source packages

See also

Using the renv package to create a project-specific set of packages

Installing and managing different versions of Bioconductor packages in environments

Using bioconda to install external tools

swGetting ready

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

Loading data from files with readr

Tidying a wide format table into a tidy table with tidyr

Tidying a long format table into a tidy table with tidyr

Combining tables using join functions

Reformatting and extracting existing data into new columns using stringr

How it works….

Computing new data columns from existing ones and applying arbitrary functions using mutate()

Using dplyr to summarize data in large tables

Using datapasta to create R objects from cut-and-paste data

Chapter 3: ggplot2 and Extensions for Publication Quality Plots

Combining many plot types in ggplot2

Comparing changes in distributions with ggridges

Customizing plots with ggeasy

Highlighting selected values in busy plots with gghighlight

Plotting variability and confidence intervals better with ggdist

Making interactive plots with plotly

Clarifying label placement with ggrepel

Zooming and making callouts from selected plot sections with facetzoom

Chapter 4: Using Quarto to Make Data-Rich Reports, Presentations, and Websites

Using Markdown and Quarto for literate computation

Creating different document formats from the same source

Creating data-rich presentations from code

Getting ready.

How to do it…

Creating websites from collections of Quarto documents

Adding interactivity with Shiny

Chapter 5: Easily Performing Statistical Tests Using Linear Models

Modeling data with a linear model

Using a linear model to compare the mean of two groups

Using a linear model and ANOVA to compare multiple groups in a single variable

Using linear models and ANOVA to compare multiple groups in multiple variables

Testing and accounting for interactions between variables in linear models

Doing tests for differences in data in two categorical variables

Making predictions using linear models

Chapter 6: Performing Quantitative RNA-seq

Estimating differential expression with edgeR

Estimating differential expression with DESeq2

There's more...

Estimating differential expression with Kallisto and Sleuth

Using Sleuth to analyze time course experiments

Analyzing splice variants with SGSeq

Performing power analysis with powsimR

Finding unannotated transcribed regions

Finding regions showing high expression ab initio using bumphunter

Differential peak analysis

Estimating batch effects with SVA

Finding allele-specific expression with AllelicImbalance

Presenting RNA-Seq data using ComplexHeatmap

Chapter 7: Finding Genetic Variants with HTS Data

Finding SNPs and INDELs from sequence data using VariantTools

Predicting open reading frames in long reference sequences

Plotting features on genetic maps with karyoploteR

Selecting and classifying variants with VariantAnnotation

Extracting information in genomic regions of interest

Finding phenotype and genotype associations with GWAS

Estimating the copy number at a locus of interest

Chapter 8: Searching Gene and Protein Sequences for Domains and Motifs

Technical requirements.

Further information

Finding DNA motifs with universalmotif

Finding protein domains with PFAM and bio3d

Finding InterPro domains

See also…

Finding transmembrane domains with tmhmm and pureseqTM

Creating figures of protein domains using drawProteins

Performing multiple alignments of proteins or genes

Aligning genomic length sequences with DECIPHER

Novel feature detection in proteins

3D structure protein alignment in bio3d

Chapter 9: Phylogenetic Analysis and Visualization

Reading and writing varied tree formats with ape and treeio

Visualizing trees of many genes quickly with ggtree

Quantifying and estimating the differences between trees with treespace

Extracting and working with subtrees using ape

Creating dot plots for alignment visualizations

Reconstructing trees from alignments using phangorn

Finding orthologue candidates using reciprocal BLASTs.

Notes:

Includes index.

Includes bibliographical references and index.

Description based on print version record.

ISBN:

9781837633821

1837633827

OCLC:

1407627208