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R Bioinformatics Cookbook

R Bioinformatics Cookbook

By : MacLean, Dr Dan Maclean
2.7 (3)
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R Bioinformatics Cookbook

R Bioinformatics Cookbook

2.7 (3)
By: MacLean, Dr Dan Maclean

Overview of this book

Handling biological data effectively requires an in-depth knowledge of machine learning techniques and computational skills, along with an understanding of how to use tools such as edgeR and DESeq. With the R Bioinformatics Cookbook, you’ll explore all this and more, tackling common and not-so-common challenges in the bioinformatics domain using real-world examples. This book will use a recipe-based approach to show you how to perform practical research and analysis in computational biology with R. You will learn how to effectively analyze your data with the latest tools in Bioconductor, ggplot, and tidyverse. The book will guide you through the essential tools in Bioconductor to help you understand and carry out protocols in RNAseq, phylogenetics, genomics, and sequence analysis. As you progress, you will get up to speed with how machine learning techniques can be used in the bioinformatics domain. You will gradually develop key computational skills such as creating reusable workflows in R Markdown and packages for code reuse. By the end of this book, you’ll have gained a solid understanding of the most important and widely used techniques in bioinformatic analysis and the tools you need to work with real biological data.
Table of Contents (13 chapters)
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Developing reusable workflows and reports

A very common task in bioinformatics is writing up our results in order to communicate them to a colleague or just to have a good record in our laboratory books (electronic or otherwise). A key skill is to make the work as reproducible as possible so that we can rerun it ourselves when we need to revisit it or someone else interested in what we did can replicate the process. One increasingly popular solution to this problem is to use literate programming techniques and executable notebooks that are a mixture of human-readable text, analytical code, and computational output rolled into a single document. In R, the rmarkdown package allows us to combine code and text in this way and create output documents in a variety of formats.

In this recipe, we'll look at the large-scale structure of one such document that can be compiled with...

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