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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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Making use of the apply family of functions

Programming in R can sometimes seem a bit tricky; the control flow and looping structures it has, are a bit more basic than in other languages. As many R functions are vectorized, the language actually has some features and functions; that mean we don't need to take the same low-level approach we may have learned in Python or other places. Instead, base R provides the apply functions to do the job of common looping tasks. These functions all have a loop inside them, meaning we don't need to specify the loop manually. In this recipe, we'll look at using some apply family functions with common data structures to loop over them and get a result. The common thread in all of the apply functions is that we have an input data structure that we're going to iterate over and some code (often wrapped in a function definition...

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