APS 61011 Advanced Biological Analysis

Level 4
Semester 2
Credits 15
Teaching Staff Professor Andrew Beckerman, Dr Dylan Childs, Dr Nicola Nadeau, Dr Alison Wright, Dr Helen Hipperson, Dr Tom Webb, Dr Pascal-Antoine Christin, Dr Gavin Thomas
Co-ordinator Professor Andrew Beckerman

Description

The aim of this module is to provide students with advanced training in the use of statistical methods and computers to visualise and analyse biological data which is necessary to pursue a research career in whole organism biology. Advanced principles of programming for data analysis, data interpretation and statistical analysis, and graphical presentation are stressed. The course is based on the statistical programming language R used through the integrated development environment RStudio. The course is comprised of 7 introductory sessions, a compulsory session on experimental design/ANOVA/ANCOVA and then a choice of two specialist modules from generalized linear models, mixed effects models, phylogenies and phylogenetic inference, Next Generation molecular data and spatial data and mapping. Students will develop skills in the use of the R Programming Language and related computing tools for data management, advanced visualization of data, statistical principles for making inference from data and best practice for reporting results of data analysis.

Aims

1. Develop students’ skills in the use of the R Programming Language and related computing tools for advanced visualisation of data and statistics.
2. Develop students’ knowledge of data management tools for biological data.
3. Develop students’ knowledge about effective visualisation of data and statistics.
4. Develop students’ knowledge of statistical principles for making inference from data and best practice for reporting results of data analysis.

Learning Outcome

By the end of the module, the student will be able to: 


1. Use the RStudio interface to the R programming language, understanding how to customise it and interact with it.
2. Compose a document called a script of R code, using RStudio, containing instructions for data management, visualisation and analysis of experiments or survey data.
3. Identify and design appropriate statistical methods for the analysis of data from a variety of observations and experiments and for different types of data.
4. Visualize and analyse several types of data common to the Biological Sciences.
5. Describe, evaluate and interpret the statistical outputs of analyses.

Student Contact Time: The teaching will be in the form of 10 workshops that integrate lectures with complementary, self-directed practicals.

Assessment Method:  100% Examination – Open Book. Two-part online exam (S1 = 20%; S2 = 80%); S2 exam specific to the mix of mandatory and choice blocks of learning.

Feedback: The students receive feedback throughout the course. 


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