Biological Data Analysis

Abstract

Statistical techniques for the analysis of biological data are introduced (analysis of variance, linear and generalized linear models, randomisation tests). Participants learn to choose appropriate methods for their questions, to handle data sets and analyse them using the software R, to represent results in tables and graphs. and to interpret them in both statistical and biological terms.

Objective

Participants will gain the ability to analyse typical data from biological research using the free statistical software R. Specifically, they will: - get familiar with statistical methods commonly used for the analysis of biological data - know what type of questions and what type of data can be analysed with these methods, as well as conditions for their correct application. - practise data handling and statistical analysis with R - understand the meaning of results in statistical and biological terms - be able to draw high-quality graphs with R

Content

Contents include statistical methods commonly used in biological studies: - data handling, description and graphical representation - analysis of variance - linear and generalized linear models - accounting for data structure and experimental design - randomisation tests The course deals with the application of these methods in biological research: - What is the purpose of the method? - What type of research questions and what type of data can be analysed with it? - What conditions have to be satisfied? - How can the analyses be done using the sofware R? - What are good representations of the data or results, and how are they obtained with R? - What do the results mean statistically and biologically? Participants are expected to have a basic statistical knowledge (data distributions, descriptive statistics, principles of statistical testing). On request, those who need to acquire or refresh this knowledge will receive material for self-study before the start of the course. No previous experience using R is required, but the schedule and content of the first day will vary according to each participant's experience (full day for beginners, reduced programme for more experienced users). The course takes place daily from 7 till 18 January 2008. The deadline for registration is 15 December 2007.

Resources