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)

Easily Performing Statistical Tests Using Linear Models

Linear models are a statistical tool used to model the relationship between a dependent variable and one or more independent variables. They are based on the assumption that the relationship between the variables is linear, meaning that the change in the dependent variable is proportional to the change in the independent variables.

Linear models are widely used in many fields, including bioinformatics. In bioinformatics, linear models can be used to analyze large datasets, such as gene expression data. For example, linear models can be used to identify differentially expressed genes between different experimental conditions or to predict the expression of genes based on other variables, such as clinical data.

Linear models are closely related to statistical tests, such as t-tests and analysis of variance (ANOVA). In fact, t-tests and ANOVA can be seen as special cases of linear models. For example, a two-sample t-test is...