Applied bioinformatics and data analysis in medical research
Ph.D. -course
- ECTS credits
- 3
- Teaching semesters
- Autumn
- Course code
- NEUROSYSM930
- Number of semesters
- 1
- Teaching language
- English
- Resources
- Schedule
Course description
Objectives and Content
Dates: 4-6 desember
The main objective of this course is to provide the candidates with the knowledge central for successful application of bioinformatics and data analysis in clinical research on human tissue.
The course will focus on practical aspects and methodological considerations that needed to be taken into account when dealing with human derived data, such as data sensitivity, limited sample sizes, sample misclassification, choice of appropriate statistical models, and covariates, and tissue heterogeneity. The course will be composed of seminars, lectures, and hands-on tutorials given by clinicians and researchers based on real-life scenarios. The course will provide an inspiring environment, promoting networking between trainees and researchers, promoting career development, and encouraging future collaborations.
The course will be highly beneficial for all participants with an interest in bioinformatics, biology, medicine, or clinical research in general.
Dr. Gonzalo S. Nido
Dr. Fiona Dick
Seminars:
Invited speakers
Learning Outcomes
Upon completing the course, the candidate will have gained knowledge of the following:
- Understanding of basic statistical models, benefits and limitations of covariate inclusion.
- Legal and practical implications of working with sensitive data.
- General understanding of possibilities and limitations of Next Generation Sequencing (NGS) data, with an emphasis on RNA and whole genome/exome sequencing.
- Biobanks both in term of access and sample collections.
- Understanding of the limitations and constraints of studies involving human tissue.
Upon completing the course, the candidate will gain the following skills:
- Big data analysis and dimensionality reduction.
- Data quality control: removal of low quality samples, detection of sample outliers and data filtering, identification of mislabeled samples.
- Basic data wrangling with R.
Upon completing the course, the candidate will gain the following competences:
- Designing experiments under constrains of scarse sample availibility.
- Descision making based on metadata and other existing information (including power estimation based on preliminary data).
ECTS Credits
Level of Study
Recommended as part of the training component for PhD candidates affiliated with the Research School of Neuro-SysMed.
The course is highly recommended for Master students towards the end of their degree, and PhD candidates in their first year. However, PhD candidates in more progressed stages of their research, and postdocs, will also greatly benefit from this course.
Semester of Instruction
Place of Instruction
Required Previous Knowledge
Recommended Previous Knowledge
Credit Reduction due to Course Overlap
Access to the Course
The course is open to students (Ph.D. candidates, master¿s and research track students), postdoctoral fellows, and researchers with a clinical/biological/bioinformatics background. The course will be open nationally and internationallyRegistration in Studentweb for internal students.
Deadline: September 1st
Forms of Assessment
Pass/ Fail
To pass the course the candidate must have:
1. Prepared for the course by reading scientific papers.
2. Participated in the course days and actively work on course tasks and made an oral presentation of own research.
3. Written a methodological reflection note (2-3 pages).
Reading List
Reading before course start:
- GDPR: an impediment to research?
(PMID: 30734900) - The FAIR Guiding Principles for scientific data management and stewardship
(PMID: 26978244) - Principles of confounder selection
(PMID: 30840181) - Small telescopes: detectability and the evaluation of replication results
(PMID: 25800521) - Tackling the widespread and critical impact of batch effects in high-throughput data
(PMID: 20838408) - RNA sequencing: the teenage years
(PMID: 31341269) - Common gene expression signatures in Parkinson's disease are driven by changes in cell composition
(PMID: 32317022) - Best practices for variant calling in clinical sequencing
(PMID: 33106175)
Suggested reading:
The list is preliminary, and will be finalized several weeks prior to the beginning of the course.
Course Coordinator
Main responsibles:
Gonzalo S. Nido
Fiona Dick
Coordinator:
Eirik Tveit Solheim
Who may participate
Programme
All tasks are obligatory in order to merited ECTS:
1. Prior to the beginning of the course, the students will receive a list of scientific papers related to the covered topics. Students are expected to critically read and familiarize themselves with the papers prior to the lectures (20 hours).
2. Each course day will begin with a seminar from a clinician or a researcher who will present their research, serving as a real case scenario for topics covered during that day. The seminar will be followed by a set of lectures, and group discussions. After a one-hour lunch break, with food provided, there will be practical hands-on tutorials. These will use real-life data, covering the daily topics, and the students will be guided and assisted by expert teachers (21 hours).
3. On the last day, the student will prepare a flash talk (5 min) describing their own project. The same project can be used for the written assignment (5 hours).
4. For the final assessment, the students will be required to write a methodological description of a project on a research topic of their choice. The project can be based on their ongoing research or any other topic of interest. The description should cover all relevant considerations and topics covered throughout the course and is to be submitted two weeks after the course (30 hours).
Total work: 76 hours