Book Image

R Bioinformatics Cookbook - Second Edition

By : Dan MacLean
Book Image

R Bioinformatics Cookbook - Second Edition

By: Dan MacLean

Overview of this book

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.
Table of Contents (16 chapters)

Estimating differential expression with DESeq2

The DEseq2 package is a popular tool for performing differential analysis of count data, so it is ideal for expression analysis of RNA-Seq data in R and other count data such as ChIPSeq.

DEseq2 performs normalization using a method called variance stabilizing transformation (VST), which is a type of transformation that aims to stabilize the variance of the data across the range of counts. This is in contrast to other normalization methods that aim to bring the mean of the data to a specific value, such as the mean of all the samples or the median of all the samples. The VST method is effective at reducing the variance of the data estimating with and improving the statistical power of differential expression analyses. This allows us to focus on improving gene ranking in results tables.

DEseq2 uses a negative binomial model to fit the count data and estimate the dispersion parameter. This model is commonly used for RNA-Seq data because...