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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

Using a linear model to compare the mean of two groups

The t-test is a statistical method used to help us decide whether there is likely to be a difference between the means of two groups. t-tests are probably the most widely used tests in bioinformatics and biology, usually applied without consideration as to whether the assumptions of the test hold and can be intepreted without criticism. By learning how to do the t-test through building a linear model, you will be able to test whether the assumptions hold since a well fit model implies a good fit to the assumptions. The t-test is a special case of the linear model because it can be framed as a linear regression problem with a binary predictor variable.

In the linear model, we try to fit a linear equation that describes the relationship between a response output variable (dependent variable) and one or more predictor input variables (independent variables). In the case of a t-test, we have one binary predictor variable, which...

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