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)

Performing power analysis with powsimR

Statistical power analysis is used to determine the sample size needed to detect an effect of a certain size with a certain level of statistical significance. This is important because it allows researchers to ensure that their studies are adequately powered (that is, enough replicates have been sampled) to detect the effects that they are interested in. Without sufficient power, there is a higher risk of failing to reject the null hypothesis when it is false – that is, to miss important differentially expressed genes. In this recipe, we’ll use the powsimR package (which is not in Bioconductor) to perform two types of power analysis. Both of these will be performed with a small real dataset. First, we shall do power analysis with two treatments, test and control, and then with just one. With each, we shall estimate the replicates that are needed to spot differences in gene expression of a particular magnitude – if they’...