DS825: Analysis of Microarray Data
Study Board of Science
Teaching language: Danish or English depending on the teacher
EKA: N340080102
Assessment: Second examiner: None
Grading: Pass/Fail
Offered in: Odense
Offered in: Autumn
Level: Master
STADS ID (UVA): N340080101
ECTS value: 2
Date of Approval: 13-05-2020
Duration: 1 semester
Version: Archive
Comment
Entry requirements
Academic preconditions
Participant limit
Course introduction
Advantaged by the rapid development in biotechnology, computer and information sciences, large scale genomic data are collected in biomedical research and even in clinical practice. Analyzing and interpreting genomic data is becoming a challenge for biomedical scientists. It is thus essential for students in data sciences to be offered the opportunity to learn the knowledge in the different types of genomic data in medicine and skills in handeling the data.
The aim of the course is to introduce microarray technology and its application in transcriptomics of human diseases from basic technique to data structure, quality control, normalization to statistical analysis, to bioinformatics analysis of functional annotations and interpretation.
The course is built upon the knowledge and competence acquired in the first year courses of data sciences and basic data analytical skills (statistics and R programming).
The course is of Level 7 (master) as a elective course.
Generel competences:
- Knowledge about the needs and opportunities of the specialization when working with and processing data
- Competences in selecting, applying, and combining the right programming, statistics, and machine learning tools and methods to work with data relevant for the specialization
- Skills in managing complex work and development situations in the areas of data processing and analysis as well as starting up and executing analyses
Expected learning outcome
The learning objective of the course is that the student demonstrates the ability to:
- Understand how microarray technique works in genomic analysis
- Know the pipeline for microarray data preprocessing, quality control, data normalization
- Become familiar with microarray experiment design and corresponding statistical analysis
- Write R scripts for conducting data analysis
- Be able to interprete the findings from the microarray analysis
Content
The following main topics are contained in the course:
- Introduction to microarray technique
- Experiment design in microarray analysis
- Raw data preprocessing, quality control, normalization
- Statistical models for microarray data analysis
- Biological pathway analysis
- Machine learning methods for prediction
- Application in biomedicine
Literature
Examination regulations
Exam element a)
Timing
January
Tests
Written assignment
EKA
N340080102
Assessment
Second examiner: None
Grading
Pass/Fail
Identification
Full name and SDU username
Language
Normally, the same as teaching language
Duration
24 hours
Examination aids
Allowed.
ECTS value
2
Additional information
The examination form for re-examination may be different from the exam form at the regular exam.
Indicative number of lessons
Teaching Method
The teaching method is based on three phase model.
- Intro phase: 2 hours
- Skills training phase: 18 hours, hereof tutorials: 12 hours and laboratory exercises: 6 hours
Activities during the study phase:
The course will be given as class lecture followed by instructed group exercises on real data. The lecture part focuses on biology, technology, big data processing, statistical modeling, etc. while the exercise part will provide computer scripts for the students and details on computing explain during the exercises. These two parts are connected but highly different in term of teaching methods and contents, each takes about half of the total course time.
Teacher responsible
| Name | Department | |
|---|---|---|
| Qihua Tan | qtan@health.sdu.dk | Epidemiologi, Biostatistik og Biodemografi (EBB) |