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 linear models and ANOVA to compare multiple groups in multiple variables

Two-way ANOVA is a statistical method used to analyze the effects of two categorical independent variables, also known as factors, on a continuous dependent variable. The two independent variables can be either fixed or random.

The main purpose of two-way ANOVA is to examine whether there is a significant interaction between the two independent variables, as well as to determine the main effects of each independent variable on the dependent variable.

The analysis involves calculating the sum of squares for each of the effects and the interaction and comparing these values to their respective degrees of freedom to obtain F ratios. The F ratios are then compared to critical values from an F-distribution to determine whether the effects are statistically significant.

Like the one-way ANOVA seen in the Using a linear model and ANOVA to compare multiple groups in a single variable recipe, the basis is...