Duration
5h Th, 15h APP
Number of credits
| Master in biomedicine, research focus | 2 crédits |
Lecturer
Language(s) of instruction
English language
Organisation and examination
Teaching in the second semester
Schedule
Units courses prerequisite and corequisite
Prerequisite or corequisite units are presented within each program
Learning unit contents
The theoretical part of this course will cover:
- The evolution of "NGS" type sequencing and associated applications
- The processing of sequencing data
- Single-Cell technology
- The "Multi-omics" approach, "Spatial" technology and imaging
- The "Batch" effect and dataset integration
- The practical part of this course will include:
- Extraction of quantifiable information
- Normalization, clustering and dimension reduction
- Batch effect correction and metadata
- Analysis of "multi-omics" data
- Processing of imaging data
Learning outcomes of the learning unit
At the end of this course, the student will be able to:
- understand the basics of sequencing data processing
- prepare the data to extract the results
- master the main steps of data analysis
- understand the impact of the batch effect and the importance of metadata
- integrate several datasets and "multi-omics" data
Prerequisite knowledge and skills
INFO9024-1
This training is based on students' prior learning of the R programming language.
Using command-line in terminal will be part of the practical courses
Planned learning activities and teaching methods
After learning the important concepts during the theoretical courses, the student will have to perform data analyses of different applications coming from NGS data. He will understand how to perform a particular analysis, and how to adapt it to different experimental models. For the practical work, the RStudio interface will be used to exploit the R programming environment.
Mode of delivery (face to face, distance learning, hybrid learning)
Face-to-face course
Further information:
Lectures and practical work in face to face
Course materials and recommended or required readings
Platform(s) used for course materials:
- Microsoft Teams
Further information:
Different files will be made available to students:
- Theoretical and practical course slides
- Important resource documents
- Datasets
- Examples of scripts
Exam(s) in session
Any session
- In-person
oral exam
Further information:
The evaluation will include an analysis to be carried out by the student. He may have different resources (defined during the courses) at his disposal during the exam.
The evaluation criteria include:
- Carrying out the analysis in its entirety (30%)
- The mastery and understanding of the key steps of the analysis carried out (50%)
- General knowledge about data analysis (20%)
Work placement(s)
Organisational remarks and main changes to the course
Contacts
Arnaud LAVERGNE, PhD
GIGA-BIOINFORMATICS
B34 - 1st Floor
+32 4 3663453
arnaud.lavergne@uliege.be