Bioinformatics: An Introduction to Solving Biological Problems with R

BeginnersPrerequisites

This course provides an introduction to the R programming language and software environment for statistical computing and graphics. A variety of examples with a biological theme will be presented. Further information is available here.

The Course Web Site providing links to the course materials is here.

Please note that if you are not eligible for a University of Cambridge Raven account you will need to book or register Interest by linking here.

• Our courses are open to all who might benefit
• Booking priority is given to people from Cambridge University and Collaborating Institutes
• Individual Course fees are required only from External participants not from Collaborating Institutes

• No prior knowledge of R, or of programming in general, will be assumed
• Some familiarity with command line UNIX would be an advantage but not essential

Number of sessions: 2

• R environment
• Informal introduction to R basics
• Introducing R data objects
• Understanding data types
• Manipulating data in R
• Basic build-in function for data manipulation
• Loops and branching as useful data handling technique
• A guide to using R for everyday data analysis
• Reading and writing data tables
• Data manipulation
• Starting out with statistical tests

• Data analysis and R automation with examples
• Data analysis 'Stepwise'. Interactive R scripting
• Structuring an R progam'
• Functions as procedures
• Functions with arguments
• Advanced data analysis and integration
• Basic R graphics
• High level plotting functions
• Customized plotting functions

To enable bench scientists with no previous programming background to perform simple statistical analyses with R

Presentations, demonstrations and practicals

Participants may find it useful to look over the R tutorial from the computational biology group, Department of Oncology

2

A number of times per year

• An Introduction to R: Software For Statistical Analysis
• Bioinformatics: R object-oriented programming and package development
• Bioinformatics: Microarray Analysis with Bioconductor
• Bioinformatics: Analysis of High-throughput sequencing data with Bioconductor