BMB831: Biostatistics in R II
The Study Board for Science
Teaching language: English
EKA: N210021112, N210021102
Assessment: Second examiner: None, Second examiner: External
Grading: Pass/Fail, 7-point grading scale
Offered in: Odense
Offered in: Autumn
Level: Master
STADS ID (UVA): N210021101
ECTS value: 5
Date of Approval: 29-04-2019
Duration: 1 semester
Version: Archive
Comment
Entry requirements
Academic preconditions
Students taking the course are expected to:
- Have knowledge in statistics
- Understand the basic principles of molecular biology
- Have basic programming skills in R
- Know the fundamentals of biostatistics
Course introduction
Modern experimental platforms nowadays deliver the quantification of pools of biological molecules. Their analysis requires complex bioinformatics pipelines to obtain biologically relevant results. The students will use the acquired knowledge to design and apply work flows that handle omics data sets. The course consists of a theoretical and an extensive practical part, with the objective to provide advanced understanding of data analysis with R scripts and application of bioinformatics tools.
The course will introduce the students to advanced programming of R scripts necessary to deal with data from modern high-throughput experiments and gives a broad overview of tools for biological interpretation. Exercises involve in-depth application of standard pipelines to process omics data and a final project to apply the acquired abilities on real data that might come from experiments previously carried out by the student, e.g. during their bachelor/master thesis.
Expected learning outcome
The learning objectives of the course are that the student demonstrates the ability to:
- independently analyze even conceptually demanding data sets.
- work with large data amounts and carry out standard statistical analysis to identify relevant features.
- use standard algorithms for multi-variate analysis
- design scripts for detailed visualization of their results.
- know and apply tools for data interpretation.
- know and apply standard pipelines for the processing of omics data.
- know how to objectively discuss applied data analysis methods presented e.g. in publications.
Content
The following main topics are contained in the course:
- statistics for large data sets
- different types of data modeling
- advanced data visualization
- advanced data interpretation
- computational tools for protein characterization
- standard work flows for data from omics experiments
Literature
Examination regulations
Prerequisites for participating in the exam a)
Timing
Autumn
Tests
Tutorial and exercises
EKA
N210021112
Assessment
Second examiner: None
Grading
Pass/Fail
Identification
Student Identification Card - Name
Language
Normally, the same as teaching language
Examination aids
To be announced during the course
ECTS value
0
Additional information
Participation at minimum 80% of tutorials
The prerequisite examination is a prerequisite for participation in exam element a)
The prerequisite examination is a prerequisite for participation in exam element a)
Exam element a)
Timing
Autumn
Prerequisites
| Type | Prerequisite name | Prerequisite course |
|---|---|---|
| Examination part | Prerequisites for participating in the exam a) | N210021101, BMB831: Biostatistics in R II |
Tests
Individual report
EKA
N210021102
Assessment
Second examiner: External
Grading
7-point grading scale
Identification
Full name and SDU username
Language
Normally, the same as teaching language
Examination aids
To be announced during the course
ECTS value
5
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
In the individual report, the student describes how they solve a given biostatistics assignment. Details on the individual assignment will be discussed during the exercises. The report should consist of at least 5 pages with a maximum of 10 pages excluding references. It should contain the following sections: a) Introduction and description of assignment; b) Overview of the student's procedure to solve the task; and c) evaluation of results and conclusion.