Clinical Data, Omic Signatures and Workflows, using R, for Biologists BIOL5421

  • Academic Session: 2023-24
  • School: School of Infection and Immunity
  • Credits: 10
  • Level: Level 5 (SCQF level 11)
  • Typically Offered: Semester 2
  • Available to Visiting Students: No

Short Description

This course builds students' proficiency in coding in R, and expands their experience to include clinical data statistics and analysis. Students will be taught how to create generic functions for regularly used analyses, as well as more detailed statistics relating to data correction and omic signature analysis to be able to make accurate inferences about 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

Biol5354

Assessment

Data correction (20 %, up to 100 words) [ILO 1]

Analysis Script file/s (30 %) [ILO 2]

Report (50 %, 2-4 multi-plot figures, 500-750 words) [ILOs 3&4]

Course Aims

This course aims to expand students experience and proficiency of statistical analysis and coding in R, using both clinical and omic data. This course will include writing functions, data correction, survival curves, K-means, and signature analysis.

Intended Learning Outcomes of Course

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

1. Critically appraise data to identify and apply appropriate correction methods

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

3. Critically explore and effectively communicate the biological results using advanced bioinformatic visualisation techniques

4. Critically appraise clinical and omic data to make appropriate inferences about underlying biology.

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.