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)

Making predictions using linear models

Linear models are commonly used in bioinformatics for prediction tasks due to their simplicity, interpretability, and ability to handle high-dimensional datasets. In bioinformatics, researchers often work with large datasets that have a large number of features (such as gene expression data or sequence data), making it challenging to analyze them with more complex models. Linear models offer a straightforward and computationally efficient way to analyze these datasets. Linear models can help researchers identify genes or genetic variants that are associated with a particular trait or disease. They can also be used in feature selection, which is an important step in bioinformatics data analysis. Feature selection aims to identify the most relevant features (genes, proteins, etc.) that are associated with the outcome of interest (disease, drug response, etc.). Linear models can be used to rank features based on their importance and select the most...