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 logistic regression to classify the relative likelihood of two outcomes

Logistic regression is a statistical modeling technique used to predict categorical outcomes, particularly in binary classification situations where the outcome can take one of two possible values. It aims to find the relationship between a set of input variables and the probability of a certain outcome occurring. Logistic regression estimates the relationship between these input variables and the probability of the outcome. It tries to find the best-fit line or curve that represents this relationship. Unlike linear regression, which predicts continuous values, logistic regression predicts the probability of a specific outcome. We set a probability threshold (usually 0.5) to decide the class label. If the predicted probability is above the threshold, the outcome is predicted as one class, and if it is below the threshold, the outcome is predicted as the other class. Once the model is trained, it can be used...