TeachingMaterial

This repository is an aggregator for various R, make and git/github teaching material. Most of the courses are taught at the University of Cambridge, UK, and some have been adapted and exported outside. We would also like to acknowledge contributions from Aleksandra Pawlik, Software Sustainability Institute, Raphael Gottardo, Fred Hutchinson Cancer Research Center and Karl Broman, University of Wisconsin-Madison.

Each material subdirectory has its own repository; TeachingMaterial aggregates a snapshot as a central entry point. Aggregation is done using git-subtree (see the administration page for details). The local copies linking to external repositories are prefixed with an underscore.

Unless otherwise stated, all material is licensed under a Creative Commons Attribution-ShareAlike 3.0 License. This means you are free to copy, distribute and transmit the work, adapt it to your needs as long as you cite its origin and, if you do redistribute it, do so under the same license.

See also the TeachingMaterial wiki for meta-information about the repository and general R installation material and links.

If you like this material and/or this initiative, do not hesitate to let us know by starring the repo, tweeting about it and sharing it with your colleagues.

Material

Mass spectrometry and proteomics using R/Bioconductor

• The R for Mass Spectrometry book introduces participants to the analysis and exploration of mass spectrometry (MS) based proteomics data using R and Bioconductor. The course will cover all levels of MS data, from raw data to identification and quantitation data, up to the statistical interpretation of a typical shotgun MS experiment and will focus on hands-on tutorials. At the end of this course, the participants will be able to manipulate MS data in R and use existing packages for their exploratory and statistical proteomics data analysis.
• https://rformassspectrometry.github.io/book/
• Author: Laurent Gatto, Sebastian Gibb, Johannes Rainer

Visualising biomolecular data

• Description: This Visualisation of biomolecular data course is aimed at people who are already familiar with the R language and syntax, and who would like to get a hands-on introduction to visualisation, with a focus on biomolecular data in general, and proteomics in particular. This course is meant to be mostly hands-on, with an intuitive understanding of the underlying techniques.
• Direct link: http://bit.ly/biomolvis
• Author: Laurent Gatto
• Original repository: https://github.com/lgatto/VisualisingBiomolecularData

A gentle introduction to git and Github

• Learning GitHub and a bit about git using a pancake recipe as an example.
• Blog post: https://lgatto.github.io/github-intro/
• Author: Laurent Gatto

Introduction to bioinformatics and data science

• Description: The WSBIM1207 course is an introduction to bioinformatics (and data science) for biology and biomedical students. It introduces bioinformatics methodology and technologies without relying on any prerequisites. The aim of this course is for students to be in a position to understand important notions of bioinformatics and tackle simple bioinformatics-related problems in R, in particular to develope simple R analysis scripts and reproducible analysis reports to interogate, visualise and understand data in a tidy tabular format.
• Direct link: http://bit.ly/WSBIM1207
• Author: Laurent Gatto

Bioinformatics

• Description: The WSBIM1322 course is teaches the basics of statistical data analysis applied to high throughput biology. It is aimed at biology and biomedical students that are already familiar with the R langauge (see the pre-requisits section below). The students will familiarise themselves with statitical learning concepts such as unsupervised and supervised learning, hypothesis testing, and extend their understanding and practive in R data structures and programming and the Bioconductor project.
• Direct link: http://bit.ly/WSBIM1322
• Author: Laurent Gatto

Advanced R programming

• Description: A two-day course taught on the 3-4 April 2017, teaching advanced techniques in writing reliable, robust code in R.
• Author: Laurent Gatto, and Robert Stojnic.
• Original repository: https://github.com/lgatto/2017-04-03-adv-r-progr-EMBL
• Content: The material provides the opportunity to gain experience and understanding of object-oriented programming, packaging your code for distribution, advanced approaches for data visualisation, unit testing, and debugging.

R debugging and robust programming

• Description: A 2-day workshop taught on the 25-26 February 2016 at the EMBL, Heidelberg. The course aims at teaching participants debugging techniques and good practice in writing reliable, robust code.
• Author: Laurent Gatto, based on previous content by Laurent Gatto and Robert Stojnic, and Advanced R, by Hadley Wickham.
• Original repository: https://github.com/lgatto/2016-02-25-adv-programming-EMBL
• Content: Part I: Coding style(s), Interactive use and programming, Environments, Tidy data, Computing on the language. Part II: Functions, Robust programming with functions, Scoping, Closures, High-level functions, Vectorisation. Part III: Defensive programming, Debbugging: techniques and tools, Condition handling: try/tryCatch, Unit testing. Part IV: Benchmarking, Profiling, Optimisation, Memory, Rcpp.
• More details: https://github.com/lgatto/2016-02-25-adv-programming-EMBL/blob/master/README.md

rbc

• Description: Software carpentry R bootcamp, Jan 7-8, 2014, Cambridge, UK and 6-7 Nov 2014, Zurich, Switzerland.
• Authors: Stephen Eglen, Laurent Gatto, Robert Stojnić and Aleksandra Pawlik
• Original repository: https://github.com/lgatto/rbc/
• Content: R programming, plotting, git/github (via software carpentry), make, shell and knitr, profiling, testing, debugging.

spr

• Description: Scientific Programming with R, MPhil in Computational Biology
• Author: Stephen Eglen with contributions from Laurent Gatto
• Original repository: https://github.com/lgatto/spr
• More details: README

Biostat-578

• Description: Bioinformatics for Big Omics Data
• Author: Raphael Gottardo, Fred Hutchinson Cancer Research Center
• Original repository: https://github.com/raphg/Biostat-578
• More details: README

github_tutorial

• Description: git/github guide
• Original repository: https://github.com/kbroman/github_tutorial
• Author: Karl Broman, University of Wisconsin-Madison
• View it here: http://kbroman.github.io/github_tutorial/

minimal_make

• Description: minimal tutorial on make
• Original repository: https://github.com/kbroman/minimal_make
• Author: Karl Broman, University of Wisconsin-Madison
• View it here: http://kbroman.github.io/minimal_make/

QuickPackage

• Description: Two brief overviews of R package creation
• Author: Laurent Gatto
• Original repository: https://github.com/lgatto/QuickPackage
• More details: README
• Download the QuickPackage and QuickPackageAndMore slides (pdf)

R package development

• Description: Developing, documenting and testing an R package
• Author: Laurent Gatto and Robert Stojnić
• Original repository: https://github.com/lgatto/RPackageDevelopment
• More details: README
• Download the pdf

Benchmarking, profiling and optimisation

• Description: Benchmarking, profiling and optimisation
• Author: Laurent Gatto
• Original repository: https://github.com/lgatto/R-bmark-prof-optim
• More details: https://github.com/lgatto/R-bmark-prof-optim#readme
• Read the [material](https://github.com/lgatto/R-bmark-prof-optim/blob/master