15 Life Sciences

15.1 Assigning cell types with SingleR

[Aaron Lun]((<https://osca.bioconductor.org/contributors.html>)

This book covers the use of SingleR, one implementation of an automated annotation method for cell type annotation.

<https://bioconductor.org/books/3.12/SingleRBook/>

15.2 Using R for Bayesian Spatial and Spatio-Temporal Health Modeling

By Andrew B. Lawson

Progressively more and more attention has been paid to how location affects health outcomes. The area of disease mapping focusses on these problems, and the Bayesian paradigm has a major role to play in the understanding of the complex interplay of context and individual predisposition in such studies of disease. Using R for Bayesian Spatial and Spatio-Temporal Health Modeling provides a major resource for those interested in applying Bayesian methodology in small area health data studies.

Paid ~$100 https://www.routledge.com/Using-R-for-Bayesian-Spatial-and-Spatio-Temporal-Health-Modeling/Lawson/p/book/9780367490126

15.3 Computational Genomics with R

Altuna Akalin

The aim of this book is to provide the fundamentals for data analysis for genomics. We developed this book based on the computational genomics courses we are giving every year.

<http://compgenomr.github.io/book/>

15.4 Data Analysis for the Life Sciences

Rafael A Irizarry and Michael I Love

Data analysis is now part of practically every research project in the life sciences. In this book we use data and computer code to teach the necessary statistical concepts and programming skills to become a data analyst. Instead of showing theory first and then applying it to toy examples, we start with actual applications.

Pay what you want for the ebook, minimum $0.00

https://leanpub.com/dataanalysisforthelifesciences

Accompanying website

15.5 Data Analysis and Visualization in R for Ecologists

François Michonneau & Auriel Fournier

Data Carpentry’s aim is to teach researchers basic concepts, skills, and tools for working with data so that they can get more done in less time, and with less pain. The lessons below were designed for those interested in working with ecology data in R.

This is an introduction to R designed for participants with no programming experience. These lessons can be taught in a day (~ 6 hours). They start with some basic information about R syntax, the RStudio interface, and move through how to import CSV files, the structure of data frames, how to deal with factors, how to add/remove rows and columns, how to calculate summary statistics from a data frame, and a brief introduction to plotting. The last lesson demonstrates how to work with databases directly from R.

This lesson assumes no prior knowledge of R or RStudio and no programming experience.

https://datacarpentry.org/R-ecology-lesson/

15.6 R for applied epidemiology and public health

EpiR authors

This handbook is produced by a collaboration of epidemiologists from around the world drawing upon experience with organizations including local, state, provincial, and national health agencies, the World Health Organization (WHO), Médecins Sans Frontières / Doctors without Borders (MSF), hospital systems, and academic institutions.

Written by epidemiologists, for epidemiologists.

https://epirhandbook.com/

15.7 Git and Github for Advanced Ecological Data Analysis

Alexa Fredston

This material was prepared for a three-hour virtual session to teach Git and Github to a graduate-level course on Advanced Ecological Data Analysis taught at Rutgers University by Malin Pinsky and Rachael Winfree. (However, the only course-specific material is Section 4; the rest should be applicable to any reader.)

https://afredston.github.io/learn-git/learn-git.html

15.8 R for Health Data Science

by Ewan Harrison and Riinu Pius

In this age of information, the manipulation, analysis and interpretation of data have become a fundamental part of professional life. Nowhere more so than in the delivery of healthcare. From the understanding of disease and the development of new treatments, to the diagnosis and management of individual patients, the use of data and technology are now an integral part of the business of healthcare.

Those working in healthcare interact daily with data, often without realising it. The conversion of this avalanche of information to useful knowledge is essential for high-quality patient care. An important part of this information revolution is the opportunity for everybody to become involved in data analysis. This democratisation is driven in part by the open source software movement – no longer do we require expensive specialised software to do this.

The statistical programming language, R, is firmly at the heart of this.

This book will take an individual with little or no experience in data science all the way through to the execution of sophisticated analyses. We emphasise the importance of truly understanding the underlying data with liberal use of plotting, rather than relying on opaque and possibly poorly understood statistical tests. There are numerous examples included that can be adapted for your own data, together with our own R packages with easy-to-use functions.

We have a lot of fun teaching this course and focus on making the material as accessible as possible. We limit equations to a minimum in favour of code, and use examples rather than lengthy explanations. We are grateful to the many individuals and students who have helped refine this book and welcome suggestions and bug reports via https://github.com/SurgicalInformatics.

This is the free electronic Bookdown version of the HealthyR book published by [Chapman & Hall/CRC](https://www.routledge.com/R-for-Health-Data-Science/Harrison-Pius/p/book/9780367428198.

https://argoshare.is.ed.ac.uk/healthyr_book/

15.9 Modern Statistics for Modern Biology

Susan Holmes, Wolfgang Huber

The aim of this book is to enable scientists working in biological research to quickly learn many of the important ideas and methods that they need to make the best of their experiments and of other available data.

https://www.huber.embl.de/msmb/

15.10 Orchestrating Single-Cell Analysis with Bioconductor

Aaron Lun, Robert Amezquita, Stephanie Hicks, Raphael Gottardo

This is the website for “Orchestrating Single-Cell Analysis with Bioconductor”, a book that teaches users some common workflows for the analysis of single-cell RNA-seq data (scRNA-seq).

https://osca.bioconductor.org/

15.11 Statistics in R for Biodiversity Conservation Paperback

by Carl Smith , Antonio Uzal , Mark Warren

A practical handbook to introduce data analysis and model fitting using R to ecologists and conservation biologists. The book is aimed at undergraduate and post-graduate students and provides access to datasets and RScript.

Paid product ~$10

https://www.amazon.co.uk/dp/B08HBLYHQL/ref=cm_sw_r_cp_apa_i_g0luFb86PXJ9Z

15.12 Numerical Ecology with R

by Daniel Borcard, François Gillet, Pierre Legendre

This new edition of Numerical Ecology with R guides readers through an applied exploration of the major methods of multivariate data analysis, as seen through the eyes of three ecologists. It provides a bridge between a textbook of numerical ecology and the implementation of this discipline in the R language. The book begins by examining some exploratory approaches.

eBook ~$60

https://www.springer.com/us/book/9783319714035

15.13 Introduction to Data Analysis with R

by Jannik Buhr

This is a video lecture series with accompanying lecture script that is designed to read much like a book. The lecture is held in English for biochemists at Heidelberg University, Germany, but the examples covered are no specific to life sciences in order to enable a focus on learning the techniques with R.

free

https://jmbuhr.de/dataIntro20/

15.14 WEHI Intro to Tidy R Course

by Brendan Ansell

A complete beginner’s introduction to tidy R for data transformation, visualization and analysis automation — with applications in experimental biology.
This book is based on a short course developed for biomedical scientists at the WEHI Medical Research Institute. The content is designed to make learners comfortable with using R for exploratory analysis of large data sets, but does not cover statistics. The material and teaching examples draw on popular (non-biological) data sets, as well as gene expression and drug screening data types.

Free

https://bookdown.org/ansellbr/WEHI_tidyR_course_book/