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)

Using Sleuth to analyze time course experiments

Multiple-condition, multiple-level experiments, such as timecourse experiments, are more difficult to analyze than simple comparisons because they involve a greater amount of data and complexity. In a timecourse experiment, for example, the goal is to understand how a biological system changes over time in response to a particular treatment or condition. This requires analyzing data from multiple timepoints and conditions, which can make the data more complex and harder to interpret.

One aspect that makes timecourse experiments more difficult is the filter function in the sleuth_prep() filter argument. This function is used to filter out low-quality or non-informative data from the analysis. The filter function works by excluding targets that are not present in a minimum percentage of samples. In a simple comparison, the filter function is relatively straightforward to apply as it is only necessary to compare two conditions and identify...