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)

Modeling data with a linear model

Linear models are a type of statistical model used to analyze the relationship between a dependent variable and one or more independent variables. In essence, they seek to fit a line that best describes the relationship between these variables, allowing us to make predictions about the dependent variable based on the values of the independent variables. The equation for a simple linear model can be written as follows:

y = β 0 + β 1 x + ε

where y is the dependent variable, x is the independent variable, β 0 and β 1 are coefficients that represent the intercept and slope of the line, respectively, and ε is the error term.

The output of a linear model typically includes the coefficients of the model, which describe the strength and direction of the relationship between the variables, as well as measures of the model’s goodness of fit, such as the R-squared value.

Linear models...