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R Bioinformatics Cookbook. Use R and Bioconductor to perform RNAseq, genomics, data visualization, and bioinformatic analysis

Język publikacji: 1
Autor:
Dan MacLean
R Bioinformatics Cookbook. Use R and Bioconductor to perform RNAseq, genomics, data visualization, and bioinformatic analysis Dan MacLean - okladka książki

R Bioinformatics Cookbook. Use R and Bioconductor to perform RNAseq, genomics, data visualization, and bioinformatic analysis Dan MacLean - okladka książki

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Bądź pierwszym, który oceni tę książkę
Stron:
316
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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.
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O autorze książki

Professor Dan MacLean has a Ph.D. in molecular biology from the University of Cambridge and gained postdoctoral experience in genomics and bioinformatics at Stanford University in California. Dan is now a Honorary Professor in the School of Computing Sciences at the University of East Anglia. He has worked in bioinformatics and plant pathogenomics, specializing in R and Bioconductor and developing analytical workflows in bioinformatics, genomics, genetics, image analysis, and proteomics at The Sainsbury Laboratory since 2006. Dan has developed and published software packages in R, Ruby, and Python with over 100,000 downloads combined.

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