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)

Doing tests for differences in data in two categorical variables

Categorical output variables, also known as response variables or dependent variables, are variables that take on discrete values from a finite set of possible outcomes. We can consider that there are two types of categorical variables: nominal and ordinal.

Ordinal variables have a natural ordering among the categories. Examples of ordinal variables include education level, income bracket, and satisfaction ratings. In linear models, ordinal variables can be represented using their numerical values or by assigning each category a numerical rank. For example, in a linear model predicting job satisfaction based on salary, the ordinal variable income bracket could be assigned a numerical rank from one to five based on the size of the income range. Ranking helps us to use the linear model framework fairly easily.

Nominal variables are variables that have no inherent order or ranking among the categories. Examples of...