Module Directory

The amount of data generated by biological experiments is increasing exponentially, mainly due to the development of new powerful technologies for the acquisition of large-scale genetic and genomic data sets. If we would compile the DNA sequence of the human genome into a book, it would be a 200,000 pages book that will take 10 years to read.

Bioinformatics became a compulsory skill for next generation biologists. In recent years, R became the programming language of choice for bioinformatics and biologists in academia and industry are currently using many tools that were developed in R. Computational Data Analysis: R for Life Sciences provides a basic introduction to programming for biologists in R and aims to provide students with the necessary programming skills and hand-on experience in performing data analysis with R. This module would be essential for further bioinformatics courses that students would take in their third year.

The aims of this module are:

• To use the command line for basic operations


• To use R in the command line and in R studio, obtain help for functions


• To understand the role of variables and how to use them and being able to use the appropriate data structure for the data (vectors, matrices, strings, lists and factors)


• To gain an understanding the role of objects and the environment


• To write functions and understand when it is needed to write a function


• To gain an understanding the role of scripts and writing scripts for any analysis


• To read and write data from files stored on the computer


• To be able to use conditionals and Boolean logic in R


• To be able to write loops and understanding when to write loops in R


• To represent data in plots and storing the plots into different file formats


• To write documentation with integrated R code


• To comment code and strategies to structure code clearly


• To perform correlation and descriptive statistics and interpret the results


• To perform statistical tests and interpret the results


• To gain an understanding which statistical test is best suited for different questions

By the end of this module, students will be expected to be able to:

1. Effectively communicate analyses by writing scripts and functions in R and commenting the code


2. Attain knowledge of the key methods for reading and writing data files in different formats into R


3. Using and critically evaluating the key plotting functionalities of R


4. Apply principles from software development to document and demonstrate how your functions and scripts should be used


5. Understand and apply functions to perform basic statistical analyses in R (correlation analysis and statistical tests)


6. Demonstrate essential transferable skills and qualities needed to work successfully as part of a team

The amount of data generated by biological experiments is increasing exponentially, mainly due to the development of new powerful technologies for the acquisition of large-scale genetic and genomic data sets. If we would compile the DNA sequence of the human genome into a book, it would be a 200,000 pages book that will take 10 years to read. Bioinformatics became a compulsory skill for next generation biologists. In recent years, R became the programming language of choice for bioinformatics and biologists in academia and industry are currently using many tools that were developed in R. Computational Data Analysis: R for Life Sciences provides a basic introduction to programming for biologists in R and aims to provide students with the necessary programming skills and hand-on experience in performing data analysis with R. This module would be essential for further bioinformatics courses that students would take in their third year.

This module will be delivered via:

• Lectures - 12h
• Workshops - 12 x 2 = 24 h