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)

Creating a tSNE and UMAP embedding

t-Distributed Stochastic Neighbor Embedding (t-SNE) and Uniform Manifold Approximation and Projection (UMAP) are both dimensionality reduction techniques commonly used in ML and data visualization.

t-SNE is a non-linear technique that aims to visualize high-dimensional data in a lower-dimensional space while preserving the local structure of the data. t-SNE is particularly useful for revealing clusters or groups of data points that may not be immediately apparent in the original high-dimensional space.

UMAP is another non-linear dimensionality reduction technique that has gained popularity due to its scalability and efficiency. UMAP also focuses on preserving local structures and employs a different mathematical method based on graph theory and optimization techniques. UMAP is often used for visualizing large datasets with millions of data points.

Both algorithms suffer from being computationally expensive, especially when dealing with large...