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

ANOVA is a statistical method used to test whether there is a significant difference between two or more groups. ANOVA compares the variance within groups to the variance between groups to determine if there is a statistically significant difference in the means of the groups. ANOVA is commonly used in experiments where a response variable is measured across several groups under different experimental conditions.

ANOVA can be used to compare gene expression levels across multiple samples under different experimental conditions, the response variable is the gene expression level, and the categorical variable is the experimental condition. ANOVA can also be used in clinical trials to compare the effectiveness of different treatments or interventions for a disease or medical condition.

Linear models can be used to perform ANOVA by fitting a linear model to the data with a categorical variable that represents...