book

R Bioinformatics Cookbook - Second Edition

Name: R Bioinformatics Cookbook - Second Edition
Author: Dan MacLean
ISBN: 9781837634279

by Dan MacLean

October 2023

Intermediate to advanced

396 pages

8h 51m

English

Packt Publishing

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R Bioinformatics Cookbook, Second Edition
ContributorsAbout the authorAbout the reviewer
Preface
Who this book is forWhat this book coversTo get the most out of this bookDownload the example code filesConventions usedGet in touchShare Your ThoughtsDownload a free PDF copy of this book
Chapter 1: Setting Up Your R Bioinformatics Working Environment
Technical requirementsFurther informationSetting up an R project in a directoryGetting readyHow to do it…How it works…There’s more…Using the here package to simplify working with pathsGetting readyHow to do it…How it works…There’s more…Using the devtools package to work with the latest non-CRAN packagesGetting readyHow to do it…How it works…There’s more…Setting up your machine for the compilation of source packagesGetting readyHow to do it…How it works…See alsoUsing the renv package to create a project-specific set of packagesGetting readyHow to do it…How it works…There’s more…Installing and managing different versions of Bioconductor packages in environmentsGetting readyHow to do it…How it works…Using bioconda to install external toolsswGetting readyHow to do it…How it works…
Chapter 2: Loading, Tidying, and Cleaning Data in the tidyverse
Technical requirementsFurther informationLoading data from files with readrGetting readyHow to do it…How it works…There’s more…See alsoTidying a wide format table into a tidy table with tidyrGetting readyHow to do it…How it works…See alsoTidying a long format table into a tidy table with tidyrGetting readyHow it works…There’s more…Combining tables using join functionsGetting readyHow to do it…How it works…Reformatting and extracting existing data into new columns using stringrGetting readyHow to do it…How it works…Computing new data columns from existing ones and applying arbitrary functions using mutate()Getting readyHow to do it…How it works…Using dplyr to summarize data in large tablesGetting readyHow to do it…How it works…Using datapasta to create R objects from cut-and-paste dataGetting readyHow to do it…How it works…There’s more…
Chapter 3: ggplot2 and Extensions for Publication Quality Plots
Technical requirementsFurther informationCombining many plot types in ggplot2Getting readyHow to do it…How it works…There’s more…Comparing changes in distributions with ggridgesGetting readyHow to do it…How it works…Customizing plots with ggeasyGetting readyHow to do it…How it works…There’s more…Highlighting selected values in busy plots with gghighlightGetting readyHow to do it…How it works…Plotting variability and confidence intervals better with ggdistGetting readyHow to do it…How it works…Making interactive plots with plotlyGetting readyHow to do it…How it works…See alsoClarifying label placement with ggrepelGetting readyHow to do it…How it works…Zooming and making callouts from selected plot sections with facetzoomGetting readyHow to do it…How it works…Getting readyHow to do it…How it works…There’s more…See also
Chapter 4: Using Quarto to Make Data-Rich Reports, Presentations, and Websites
Technical requirementsFurther informationUsing Markdown and Quarto for literate computationGetting readyHow to do it…How it works…There’s more…Creating different document formats from the same sourceGetting readyHow to do it…How it works…Creating data-rich presentations from codeGetting readyHow to do it…How it works…There’s more…Creating websites from collections of Quarto documentsGetting readyHow to do it…How it works…There’s more…See alsoAdding interactivity with ShinyGetting readyHow to do it…How it works…There’s more…See also
Chapter 5: Easily Performing Statistical Tests Using Linear Models
Technical requirementsFurther informationModeling data with a linear modelGetting readyHow to do it…How it works…There’s more…Using a linear model to compare the mean of two groupsGetting readyHow to do it…How it works…There’s more…Using a linear model and ANOVA to compare multiple groups in a single variableGetting readyHow to do it…How it works…There’s more…Using linear models and ANOVA to compare multiple groups in multiple variablesGetting readyHow to do it…How it works…Testing and accounting for interactions between variables in linear modelsGetting readyHow to do it…How it works…Doing tests for differences in data in two categorical variablesGetting readyHow to do it…How it works…Making predictions using linear modelsGetting readyHow to do it…How it works…See also
Chapter 6: Performing Quantitative RNA-seq
Technical requirementsFurther informationEstimating differential expression with edgeRGetting readyHow to do it…How it works…Estimating differential expression with DESeq2Getting readyHow to do it…How it works…There’s more...Estimating differential expression with Kallisto and SleuthGetting readyHow to do it…How it works…Using Sleuth to analyze time course experimentsGetting readyHow to do it…How it works…Analyzing splice variants with SGSeqGetting readyHow to do it…How it works…Performing power analysis with powsimRGetting readyHow to do it…How it works…There’s more…Finding unannotated transcribed regionsGetting readyHow to do it…How it works…There’s more…Finding regions showing high expression ab initio using bumphunterGetting readyHow to do it…How it works…There’s more…Differential peak analysisGetting readyHow to do it…How it works…Estimating batch effects with SVAGetting readyHow to do it…How it works…Finding allele-specific expression with AllelicImbalanceGetting readyHow to do it…How it works…There’s more…Presenting RNA-Seq data using ComplexHeatmapGetting readyHow to do it…How it works…
Chapter 7: Finding Genetic Variants with HTS Data
Technical requirementsFurther informationFinding SNPs and INDELs from sequence data using VariantToolsGetting readyHow to do it…How it works…There’s more…See alsoPredicting open reading frames in long reference sequencesGetting readyHow to do it…How it works…There’s more…Plotting features on genetic maps with karyoploteRGetting readyHow to do it…How it works…There’s more…See alsoSelecting and classifying variants with VariantAnnotationGetting readyHow to do it…How it works…See alsoExtracting information in genomic regions of interestGetting readyHow to do it…How it works…There’s more…Finding phenotype and genotype associations with GWASGetting readyHow to do it…How it works…Estimating the copy number at a locus of interestGetting readyHow to do it…How it works…See also
Chapter 8: Searching Gene and Protein Sequences for Domains and Motifs
Technical requirementsFurther informationFinding DNA motifs with universalmotifGetting readyHow to do it…How it works…There’s more…Finding protein domains with PFAM and bio3dGetting readyHow to do it…How it works…There’s more…Finding InterPro domainsGetting readyHow to do it…How it works…There’s more…See also…Finding transmembrane domains with tmhmm and pureseqTMGetting readyHow to do it…How it works…There’s more…See alsoCreating figures of protein domains using drawProteinsGetting readyHow to do it…How it works…There’s more…Performing multiple alignments of proteins or genesGetting readyHow to do it…How it works…There’s more…Aligning genomic length sequences with DECIPHERGetting readyHow to do it…How it works…Novel feature detection in proteinsGetting readyHow to do it…How it works…3D structure protein alignment in bio3dGetting readyHow to do it…How it works…There’s more…

Chapter 9: Phylogenetic Analysis and Visualization
Technical requirementsFurther informationReading and writing varied tree formats with ape and treeioGetting readyHow to do it…How it works…See alsoVisualizing trees of many genes quickly with ggtreeGetting readyHow to do it…How it works…There’s more…Quantifying and estimating the differences between trees with treespaceGetting readyHow to do it…How it works…There’s more…Extracting and working with subtrees using apeGetting readyHow to do it…How it works…There’s more…Creating dot plots for alignment visualizationsGetting readyHow to do it…How it works…Reconstructing trees from alignments using phangornGetting readyHow to do it…How it works…Finding orthologue candidates using reciprocal BLASTsGetting readyHow to do it…How it works…See also
Chapter 10: Analyzing Gene Annotations
Technical requirementsFurther informationRetrieving gene and genome annotations from BioMartGetting readyHow to do it…How it works…Getting Gene Ontology information for functional analysis from appropriate databasesGetting readyHow to do it…How it works…Using AnnoDB packages for genome annotationGetting readyHow to do it…How it works…See alsoUsing ClusterProfiler for determining GO enrichment in clustersGetting readyHow to do it…How it works…Finding GO enrichment in an Ontology Conditional way with topGOGetting readyHow to do it…How it works…Finding enriched KEGG pathwaysGetting readyHow to do it…How it works…Retrieving and working with SNPsGetting readyHow to do it…How it works…There’s more…
Chapter 11: Machine Learning with mlr3
Technical requirementsFurther informationDefining a task and learner to implement k-nearest neighbors (k-NNs) in mlr3Getting readyHow to do it…How it works…There’s more…See also...Testing the fit of the model using cross-validationGetting readyHow to do it…How it works…There’s more…Using logistic regression to classify the relative likelihood of two outcomesGetting readyHow to do it…How it works…See alsoClassifying using random forest and interpreting it with imlGetting readyHow to do it…How it works…Dimension reduction with PCA in mlr3 pipelinesGetting readyHow to do it…How it works…There’s more…Creating a tSNE and UMAP embeddingGetting readyHow to do it…How it works…Clustering with k-means and hierarchical clusteringGetting readyHow to do it…How it works…
Chapter 12: Functional Programming with purrr and base R
Technical requirementsFurther informationMaking base R objects “tidy”Getting readyHow to do it…How it works…Using nested dataframes for functional programmingGetting readyHow to do it…How it works…See alsoUsing the apply family of functionsGetting readyHow to do it…How it works…Using the map family of functions in purrrGetting readyHow to do it…How it works…Working with lists in purrrGetting readyHow to do it…How it works…
Chapter 13: Turbo-Charging Development in R with ChatGPT
Technical requirementsFurther informationInterpreting complicated code with ChatGPT assistanceGetting readyHow to do it…How it works…There’s more…Debugging and improving code with ChatGPTGetting readyHow to do it…How it works…Generating code with ChatGPTGetting readyHow to do it…How it works…There’s more…Writing documentation for R functions with ChatGPTGetting readyHow to do it…How it works…There’s more…Writing unit tests for R functions with ChatGPTGetting readyHow to do it…How it works…Finding R packages to build a workflow with ChatGPTGetting readyHow to do it…How it works…
Index
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Overview

R Bioinformatics Cookbook is your guide to leveraging the power of R for advanced bioinformatics tasks. This updated second edition uses a recipe-based method to teach data analysis, visualization, and machine learning tailored for biological datasets. You'll gain hands-on experience with popular tools like Bioconductor, ggplot2, and tidyverse to solve real-world genomics problems.

What this Book will help me do

Set up a reproducible bioinformatics analysis environment using R.
Clean, analyze, and visualize biological data with R's powerful packages.
Apply RNA-seq and ChIP-seq workflows to study genetic information effectively.
Incorporate machine learning techniques into bioinformatics pipelines using R.
Automate tasks and create professional-grade reports using functional programming and reporting tools.

Author(s)

The author, None MacLean, brings years of expertise in bioinformatics and computational biology. Known for clear explanations and practical approaches, they ensure the material is accessible yet challenging. With a strong focus on real-world applications, this book reflects their commitment to bridging bioinformatics and modern data science.

Who is it for?

This book is perfect for bioinformaticians, researchers, and data scientists with prior R experience. It's tailored for those looking to delve deeper into genomics, data visualization, and bioinformatics techniques. Intermediate knowledge of bioinformatics concepts and familiarity with R programming are assumed for readers to fully benefit from the content.

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