Postgraduate Course: Core Data Analysis and Presentation Skills in Cancer Research (BIME11183)
|School||Deanery of Biomedical Sciences
||College||College of Medicine and Veterinary Medicine
|Credit level (Normal year taken)||SCQF Level 11 (Postgraduate)
|Course type||Online Distance Learning
||Availability||Not available to visiting students
|Summary||This course is designed to give students an understanding of the principles of scientific thinking and skills in the core topics of statistics, data analysis, experimental design, figure preparation, science communication, public engagement and project planning. The course will be built around analysis and presentation of a transcriptomics dataset. Students will learn basic programming in R and work in groups to analyse a training Affymetrix dataset. They will learn basic bioinformatics, figure preparation and communication skills to further analyse the data and present their findings to both a scientific and a lay audience. They will learn scientific writing skills to write a small summary of this mini-project and develop new hypothesis based on the findings, which will be developed in a project proposal. By combining these elements, we aim to deliver an authentic learning experience that will give the students a flavour of cancer research and begin to prepare them for future studies.
Academic description and outline content
Knowledge of the core principles of research not only allows students to conduct their own research to a high degree of professionalism, but also gives them the understanding required to critically evaluate data generated by others. This course aims to provide students with an understanding of the principles of research design and equip them with the core skills required to conduct cancer research projects in accordance with best research practice and academic integrity. The course is based around a transcriptomics dataset and will be divided into three parts:
Part 1: The first section of the course is adapted from a tutorial that forms part of the MSc in Quantitative Genetics and Genome Analysis and has been tested online in a student setting. Students will be given a training dataset from a cancer relevant, Affymetrix transcriptomics study. Using the dataset as a basis, they will be taught basic experimental design and quality control principles such as background detection, transformation and normalisation. They will also be taught how to use the R statistical package and in groups, will analyse the data and perform bioinformatic analysis. Teaching will be through online tutorials, recorded lectures, discussion boards and drop in computer sessions.
Part 2: Part 2 of the syllabus will cover figure preparation, scientific writing, scientific communication, public engagement and project design. Students will learn through IAD courses, recorded lectures, sound cloud clips and a variety of exercises using the data generated in part 1. Students will generate new hypothesis based on their analysis of the omics data (or a published manuscript describing omic analysis) and write a project proposal.
Entry Requirements (not applicable to Visiting Students)
||Other requirements|| None
Course Delivery Information
|Academic year 2022/23, Not available to visiting students (SS1)
|Course Start Date
|Learning and Teaching activities (Further Info)
Seminar/Tutorial Hours 30,
Online Activities 40,
Programme Level Learning and Teaching Hours 4,
Directed Learning and Independent Learning Hours
|Assessment (Further Info)
|Additional Information (Assessment)
Oral presentation reflecting on the experimental setup of the Omics experiment, process of analysing the data, presentation of the results and conclusions that can be drawn (40%).
Academic press release (Lay presentation of Omics experiment/results) (20%)
Project proposal (40%)
||Summative assessment consists of a written element, worth 60% of the total mark, and an online element worth 40%. In both cases, comprehensive written feedback is provided individually with 15 working days of the assessment deadline. Students are expected to reflect on their feedback, to seek additional clarification if appropriate, and to use this to improve on future assignments of a similar nature.
Formative assessment will consist of discussion around what is expected of each piece of assessed work for the course. This is conducted in an open discussion forum for all students to contribute to and provides an opportunity to clearly understand the key requirements for each assignment before submission. The outcomes of the data-analysis aspect of the course will also be formatively assessed.
|No Exam Information
On completion of this course, the student will be able to:
- Develop the skills to analyse and interpret "big data" using a variety of applications.
- Develop the skills to present scientific data in multiple forms to multiple audiences
- Synthesise and communicate the scientific challenge of cancer research, with identification of contemporary areas of advancement.
- Draw conclusions from primary research data and develop new hypothesis.
|Much of the focus of this course will be driven through student engagement. Resources will be provided as a starting point from which it is expected that students will begin to develop their own reading lists and share this information with others.|
|Graduate Attributes and Skills
||The course will provide opportunities for students to develop their skills in data and statistical analysis, bioinformatics, scientific writing, critical thinking and in their ability to communicate effectively with others through the emphasis placed upon group work and project presentation.
The course will provide opportunities for students to develop skills in research and enquiry, to identify and creatively tackle problems, and to seek out opportunities for learning.
The independent study aspect and assessments will enhance the graduates self-motivation, time management and ability to reflect on their learning. They will also improve students ability to assimilate the findings of primary research and peer knowledge in their arguments, discussions and assessments.
|Keywords||Core skills,experimental design,data analysis,programming in R,scientific writing
|Course organiser||Dr Lesley Stark
Tel: (0131) 332 2471
|Course secretary||Ms Deborah Walker
Tel: (0131 6)51 1513