Omic Data Analysis and Visualisation, using R, for Biologists BIOL5354

  • Academic Session: 2024-25
  • School: School of Infection and Immunity
  • Credits: 10
  • Level: Level 5 (SCQF level 11)
  • Typically Offered: Semester 2
  • Available to Visiting Students: No
  • Collaborative Online International Learning: No

Short Description

This course provides students who have no prior coding experience with practice in omic data analysis and visualisation using programming in the R language. This will involve teaching students how to parse data in R/RStudio to create publication quality figures to allow interpretation of the underlying biology.

Timetable

This course consists of lectures, tutorials, and computing laboratories in semester 2.

Requirements of Entry

None

Excluded Courses

None

Co-requisites

None

Assessment

Script file (20 %) [ILO 1]

Plots (40 %) [ILO 2]

Analysis of Dataset (500-750 words, 40 %) [ILO 3]

Course Aims

This course aims to introduce students to bioinformatic data analysis in R programming, using common packages and statistical analysis. This course will introduce parsing, exploring, and visualising omic data as well as the general qualities of small/big, and low/high dimensional data. This course will provide practice in using these methods to provide a foundation understanding for future project and industry placements.

Intended Learning Outcomes of Course

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

1. Demonstrate best practice of coding in R, with particular focus on the manipulation of omic data files through statistical analysis, transformations, and plot generation.

2. Critically explore and effectively communicate the biological results from big and high dimensional datasets using established bioinformatic visualisation techniques

3. Critically appraise omic data results in terms of their biological meaning 

Minimum Requirement for Award of Credits

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