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 to compare the mean of two groups

The t-test is a statistical method used to help us decide whether there is likely to be a difference between the means of two groups. t-tests are probably the most widely used tests in bioinformatics and biology, usually applied without consideration as to whether the assumptions of the test hold and can be intepreted without criticism. By learning how to do the t-test through building a linear model, you will be able to test whether the assumptions hold since a well fit model implies a good fit to the assumptions. The t-test is a special case of the linear model because it can be framed as a linear regression problem with a binary predictor variable.

In the linear model, we try to fit a linear equation that describes the relationship between a response output variable (dependent variable) and one or more predictor input variables (independent variables). In the case of a t-test, we have one binary predictor variable, which...