Statistics and data analysis form the core of much of modern biology and are fundamental to bioinformatic approaches to asking quantitative biological questions. We shall provide theoretical and hands-on practical experience of the most important aspects of statistical thinking for application in bioinformatics and the implementation of these approaches using computers. The course will cover statistical concepts, the basics of R, the use of R to run analyses, and the presentation of results of such analyses graphically.

The course will run during the first half of Semester 1. Contact teaching for this course will take place over several weeks and will consist of a mixture of lecture and computer practical sessions.

None

a) a set exercise (20%) (ILO 1,2,3 and 4)

b) a written assignment in the form of a scientific report (80%) 1500 words (+/-500 words) (ILO 1,2,3 and 4).

The aims of this course are to foster:

■ an extensive, critical and integrative understanding of statistical concepts in molecular biology

■ familiarity with the statistical programming language, R

■ acquisition of practical computing skills using R to handle, analyse, visualise and interpret bioinformatic data

■ an appreciation of how to incorporate plots and statistical results appropriately into scientific reporting of bioinformatic investigations

Students will have the chance to put these analysis concepts and approaches into practice during extensive computer lab practicals and will develop analytical skills, practical computing skills and the ability to assess critically, and in the appropriate biological context, procedures for the analysis of quantitative biological data.

By the end of this course students will be able to:

■ Discuss and critically appraise the deployment of statistical concepts in answering biological questions;

■ Critically discuss the role of R, and execute R code, in the parsing, analysis and visualisation of bioinformatic datasets; 

■ Discuss and plan appropriate data analysis approaches involving statistics in a range of circumstances relating to bioinformatic investigations, and critically interpret, evaluate and draw biological inferences from the output of these analyses;

■ Present statistical and graphical results from analyses of bioinformatic data within a written scientific report.

Students must submit at least 75% by weight of the components (including examinations) of the course's summative assessment.