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R Bioinformatics Cookbook

You're reading from   R Bioinformatics Cookbook Utilize R packages for bioinformatics, genomics, data science, and machine learning

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Product type Paperback
Published in Oct 2023
Publisher Packt
ISBN-13 9781837634279
Length 396 pages
Edition 2nd Edition
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Author (1):
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Dan MacLean Dan MacLean
Author Profile Icon Dan MacLean
Dan MacLean
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Toc

Table of Contents (16) Chapters Close

Preface 1. Chapter 1: Setting Up Your R Bioinformatics Working Environment 2. Chapter 2: Loading, Tidying, and Cleaning Data in the tidyverse FREE CHAPTER 3. Chapter 3: ggplot2 and Extensions for Publication Quality Plots 4. Chapter 4: Using Quarto to Make Data-Rich Reports, Presentations, and Websites 5. Chapter 5: Easily Performing Statistical Tests Using Linear Models 6. Chapter 6: Performing Quantitative RNA-seq 7. Chapter 7: Finding Genetic Variants with HTS Data 8. Chapter 8: Searching Gene and Protein Sequences for Domains and Motifs 9. Chapter 9: Phylogenetic Analysis and Visualization 10. Chapter 10: Analyzing Gene Annotations 11. Chapter 11: Machine Learning with mlr3 12. Chapter 12: Functional Programming with purrr and base R 13. Chapter 13: Turbo-Charging Development in R with ChatGPT 14. Index 15. Other Books You May Enjoy

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...

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