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)

Estimating batch effects with SVA

Batch effects occur in scientific experiments when there are systematic differences in the measurements that are made between different groups of samples, even though the samples themselves are biologically the same. These differences can be caused by various factors, such as differences in the lab conditions, the equipment used, or the time of the experiment. In RNA-Seq experiments, batch effects can occur when samples are run on different sequencing platforms or at different times, leading to differences in the read counts between samples. This can affect the statistical power of the experiment, as well as introduce bias into the analysis.

One common approach to address batch effects in RNA-Seq experiments is to use the surrogate variable analysis (SVA) Bioconductor package. The SVA package uses a statistical method to identify and correct the batch effects by identifying sources of variation in the data that are likely to be caused by technical...