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)

Dimension reduction with PCA in mlr3 pipelines

Principal Component Analysis (PCA) is a dimensionality reduction technique commonly used in bioinformatics to analyze and interpret high-dimensional biological data, such as gene expression data, protein profiles, or genomic data.

The main goal of PCA is to find a lower-dimensional representation of the data while preserving the most important patterns and variability present in the original data. It achieves this by transforming the data into a new set of uncorrelated variables called principal components. These principal components are ordered in such a way that the first component captures the maximum amount of variance in the data, the second component captures the second maximum variance, and so on.

PCA is useful in bioinformatics for various applications, including visualization, as it reduces data to two or three dimensions for use in plots. PCA has an important role in feature selection in ML, as it can be used to select...